{
 "cells": [
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   "source": [
    "<a id=\"table\"></a>\n",
    "<h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Table of Contents</h1>\n",
    "\n",
    "[1. Notebook Versions](#1)\n",
    "\n",
    "[2. Loading Libraries](#2)\n",
    "\n",
    "[3. Reading Data Files](#3)\n",
    "\n",
    "[4. Data Exploration](#4)\n",
    "\n",
    "[5. Baseline Modeling 1.0](#5)\n",
    "\n",
    "[6. Baseline Modeling 2.0](#6)\n",
    "\n",
    "[7. Baseline Modeling 3.0](#7)\n",
    "\n",
    "[8. Baseline Modeling 4.0](#8)\n",
    "\n",
    "[9. Ensemble Modeling ](#9)\n",
    "\n",
    "[10. Baseline Modeling 5.0](#10)\n",
    "\n",
    "<a id=\"1\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Notebook Versions</h1>\n",
    "\n",
    "- Version 1 (11/13/2023)\n",
    "    * EDA \n",
    "    * Baseline modeling 1.0.\n",
    "    \n",
    "\n",
    "- Version 2 (11/14/2023)\n",
    "    * Baseline modeling 1.0 models were updated.\n",
    "    \n",
    "    \n",
    "- Version 3 (11/14/2023)\n",
    "    * Baseline modeling 1.0 models were updated.\n",
    "    \n",
    "    \n",
    "- Version 4 (11/16/2023)\n",
    "    * Baseline modeling 2.0 added.\n",
    "    \n",
    "    \n",
    "- Version 5 (11/19/2023)\n",
    "    * Baseline modeling 2.0 updated.\n",
    "    \n",
    "    \n",
    "- Version 6 (11/20/2023)\n",
    "    * Baseline modeling 2.0 updated (median stacker)\n",
    "    \n",
    "    \n",
    "- Version 7 (11/23/2023)\n",
    "    * Baseline modeling 3.0 added.\n",
    "    * Ensemble modeling added.\n",
    "    \n",
    "    \n",
    "- Version 8 (11/24/2023)\n",
    "    * Baseline modeling 1.0 ensemble updated.\n",
    "    \n",
    "    \n",
    "- Version 9 (11/27/2023)\n",
    "    * Baseline modeling 4.0 added.\n",
    "    * Ensemble modeling updated.\n",
    "    \n",
    "    \n",
    "- Version 10 (11/28/2023)\n",
    "    * Baseline modeling 5.0 added.\n",
    "    \n",
    "    \n",
    "<a id=\"2\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Loading Libraries</h1>    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e9eb8e7",
   "metadata": {
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     "iopub.status.idle": "2023-11-28T17:21:31.513385Z",
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     "exception": false,
     "start_time": "2023-11-28T17:21:17.425834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-lego\r\n",
      "  Obtaining dependency information for scikit-lego from https://files.pythonhosted.org/packages/ee/c7/59d85703bfcee1bed406f0cb4bcb10871e5327ba333ff0557d3a100014bc/scikit_lego-0.6.16-py2.py3-none-any.whl.metadata\r\n",
      "  Downloading scikit_lego-0.6.16-py2.py3-none-any.whl.metadata (11 kB)\r\n",
      "Requirement already satisfied: scikit-learn>=1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-lego) (1.2.2)\r\n",
      "Requirement already satisfied: pandas>=1.1.5 in /opt/conda/lib/python3.10/site-packages (from scikit-lego) (2.0.3)\r\n",
      "Requirement already satisfied: patsy>=0.5.1 in /opt/conda/lib/python3.10/site-packages (from scikit-lego) (0.5.3)\r\n",
      "Collecting autograd>=1.2 (from scikit-lego)\r\n",
      "  Obtaining dependency information for autograd>=1.2 from https://files.pythonhosted.org/packages/81/70/d5c7c2a458b8be96495c8b1634c2155beab58cbe864b7a9a5c06c2e52520/autograd-1.6.2-py3-none-any.whl.metadata\r\n",
      "  Downloading autograd-1.6.2-py3-none-any.whl.metadata (706 bytes)\r\n",
      "Requirement already satisfied: Deprecated>=1.2.6 in /opt/conda/lib/python3.10/site-packages (from scikit-lego) (1.2.14)\r\n",
      "Requirement already satisfied: umap-learn>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from scikit-lego) (0.5.4)\r\n",
      "Requirement already satisfied: numpy>=1.12 in /opt/conda/lib/python3.10/site-packages (from autograd>=1.2->scikit-lego) (1.24.3)\r\n",
      "Requirement already satisfied: future>=0.15.2 in /opt/conda/lib/python3.10/site-packages (from autograd>=1.2->scikit-lego) (0.18.3)\r\n",
      "Requirement already satisfied: wrapt<2,>=1.10 in /opt/conda/lib/python3.10/site-packages (from Deprecated>=1.2.6->scikit-lego) (1.15.0)\r\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.5->scikit-lego) (2.8.2)\r\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.5->scikit-lego) (2023.3)\r\n",
      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.1.5->scikit-lego) (2023.3)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from patsy>=0.5.1->scikit-lego) (1.16.0)\r\n",
      "Requirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0->scikit-lego) (1.11.3)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0->scikit-lego) (1.3.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn>=1.0->scikit-lego) (3.2.0)\r\n",
      "Requirement already satisfied: numba>=0.51.2 in /opt/conda/lib/python3.10/site-packages (from umap-learn>=0.4.6->scikit-lego) (0.57.1)\r\n",
      "Requirement already satisfied: pynndescent>=0.5 in /opt/conda/lib/python3.10/site-packages (from umap-learn>=0.4.6->scikit-lego) (0.5.10)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from umap-learn>=0.4.6->scikit-lego) (4.66.1)\r\n",
      "Requirement already satisfied: tbb>=2019.0 in /opt/conda/lib/python3.10/site-packages (from umap-learn>=0.4.6->scikit-lego) (2021.10.0)\r\n",
      "Requirement already satisfied: llvmlite<0.41,>=0.40.0dev0 in /opt/conda/lib/python3.10/site-packages (from numba>=0.51.2->umap-learn>=0.4.6->scikit-lego) (0.40.1)\r\n",
      "Downloading scikit_lego-0.6.16-py2.py3-none-any.whl (229 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m229.1/229.1 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading autograd-1.6.2-py3-none-any.whl (49 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.3/49.3 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: autograd, scikit-lego\r\n",
      "Successfully installed autograd-1.6.2 scikit-lego-0.6.16\r\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-lego"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bb7b24f",
   "metadata": {
    "execution": {
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     "start_time": "2023-11-28T17:21:31.533958",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd; pd.set_option('display.max_columns', 100)\n",
    "import numpy as np\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import re\n",
    "\n",
    "from functools import partial\n",
    "from scipy.stats import mode\n",
    "\n",
    "import matplotlib.pyplot as plt; plt.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder, FunctionTransformer, PowerTransformer, PolynomialFeatures, RobustScaler\n",
    "from sklearn.pipeline import make_pipeline, Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV, RepeatedStratifiedKFold, cross_val_score, cross_val_predict, RepeatedKFold\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, RocCurveDisplay, cohen_kappa_score, log_loss, f1_score, median_absolute_error, accuracy_score\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
    "from sklearn.feature_selection import RFE, RFECV\n",
    "from sklearn.isotonic import IsotonicRegression\n",
    "from sklearn.calibration import CalibrationDisplay\n",
    "from sklearn.inspection import PartialDependenceDisplay, permutation_importance\n",
    "from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor, GradientBoostingRegressor, ExtraTreesRegressor, VotingRegressor, StackingRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVR\n",
    "from lightgbm import LGBMRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from sklego.linear_model import LADRegression\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import tensorflow_probability as tfp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f629fb",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"3\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Reading Data Files</h1> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a426bd6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:53.329017Z",
     "iopub.status.busy": "2023-11-28T17:21:53.328137Z",
     "iopub.status.idle": "2023-11-28T17:21:53.418096Z",
     "shell.execute_reply": "2023-11-28T17:21:53.416890Z"
    },
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     "exception": false,
     "start_time": "2023-11-28T17:21:53.308361",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dimension of the train dataset is (10407, 13)\n",
      "The dimension of the test dataset is (6939, 12)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('../input/playground-series-s3e25/train.csv')\n",
    "test = pd.read_csv('../input/playground-series-s3e25/test.csv')\n",
    "submission = pd.read_csv('../input/playground-series-s3e25/sample_submission.csv')\n",
    "\n",
    "print(f\"The dimension of the train dataset is {train.shape}\")\n",
    "print(f\"The dimension of the test dataset is {test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a6165ff0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:53.459636Z",
     "iopub.status.busy": "2023-11-28T17:21:53.459231Z",
     "iopub.status.idle": "2023-11-28T17:21:53.528089Z",
     "shell.execute_reply": "2023-11-28T17:21:53.526916Z"
    },
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     "exception": false,
     "start_time": "2023-11-28T17:21:53.439143",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>allelectrons_Total</th>\n",
       "      <th>density_Total</th>\n",
       "      <th>allelectrons_Average</th>\n",
       "      <th>val_e_Average</th>\n",
       "      <th>atomicweight_Average</th>\n",
       "      <th>ionenergy_Average</th>\n",
       "      <th>el_neg_chi_Average</th>\n",
       "      <th>R_vdw_element_Average</th>\n",
       "      <th>R_cov_element_Average</th>\n",
       "      <th>zaratio_Average</th>\n",
       "      <th>density_Average</th>\n",
       "      <th>Hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10407.00000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "      <td>10407.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5203.00000</td>\n",
       "      <td>128.053516</td>\n",
       "      <td>14.491342</td>\n",
       "      <td>17.033222</td>\n",
       "      <td>4.546789</td>\n",
       "      <td>37.507703</td>\n",
       "      <td>10.938308</td>\n",
       "      <td>2.607662</td>\n",
       "      <td>1.731330</td>\n",
       "      <td>0.944132</td>\n",
       "      <td>0.493349</td>\n",
       "      <td>2.132984</td>\n",
       "      <td>4.647126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3004.38646</td>\n",
       "      <td>224.123776</td>\n",
       "      <td>15.972877</td>\n",
       "      <td>10.468734</td>\n",
       "      <td>0.690864</td>\n",
       "      <td>26.012313</td>\n",
       "      <td>1.408276</td>\n",
       "      <td>0.334906</td>\n",
       "      <td>0.192481</td>\n",
       "      <td>0.180017</td>\n",
       "      <td>0.063080</td>\n",
       "      <td>1.936656</td>\n",
       "      <td>1.680525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2601.50000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>7.558488</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.298893</td>\n",
       "      <td>10.590660</td>\n",
       "      <td>2.530000</td>\n",
       "      <td>1.672500</td>\n",
       "      <td>0.864000</td>\n",
       "      <td>0.476196</td>\n",
       "      <td>0.814800</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5203.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.650000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>4.714286</td>\n",
       "      <td>26.203827</td>\n",
       "      <td>11.202760</td>\n",
       "      <td>2.706000</td>\n",
       "      <td>1.732727</td>\n",
       "      <td>0.915556</td>\n",
       "      <td>0.488550</td>\n",
       "      <td>1.351550</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7804.50000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>16.676996</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>48.719500</td>\n",
       "      <td>11.670725</td>\n",
       "      <td>2.805000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.981667</td>\n",
       "      <td>0.496070</td>\n",
       "      <td>2.741550</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10406.00000</td>\n",
       "      <td>15300.000000</td>\n",
       "      <td>643.093804</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>167.400000</td>\n",
       "      <td>15.245810</td>\n",
       "      <td>3.443000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.615840</td>\n",
       "      <td>0.825990</td>\n",
       "      <td>10.970000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  allelectrons_Total  density_Total  allelectrons_Average  \\\n",
       "count  10407.00000        10407.000000   10407.000000          10407.000000   \n",
       "mean    5203.00000          128.053516      14.491342             17.033222   \n",
       "std     3004.38646          224.123776      15.972877             10.468734   \n",
       "min        0.00000            0.000000       0.000000              0.000000   \n",
       "25%     2601.50000           68.000000       7.558488             10.000000   \n",
       "50%     5203.00000          100.000000      10.650000             12.600000   \n",
       "75%     7804.50000          131.000000      16.676996             22.000000   \n",
       "max    10406.00000        15300.000000     643.093804             67.000000   \n",
       "\n",
       "       val_e_Average  atomicweight_Average  ionenergy_Average  \\\n",
       "count   10407.000000          10407.000000       10407.000000   \n",
       "mean        4.546789             37.507703          10.938308   \n",
       "std         0.690864             26.012313           1.408276   \n",
       "min         0.000000              0.000000           0.000000   \n",
       "25%         4.000000             20.298893          10.590660   \n",
       "50%         4.714286             26.203827          11.202760   \n",
       "75%         4.800000             48.719500          11.670725   \n",
       "max         6.000000            167.400000          15.245810   \n",
       "\n",
       "       el_neg_chi_Average  R_vdw_element_Average  R_cov_element_Average  \\\n",
       "count        10407.000000           10407.000000           10407.000000   \n",
       "mean             2.607662               1.731330               0.944132   \n",
       "std              0.334906               0.192481               0.180017   \n",
       "min              0.000000               0.000000               0.000000   \n",
       "25%              2.530000               1.672500               0.864000   \n",
       "50%              2.706000               1.732727               0.915556   \n",
       "75%              2.805000               1.800000               0.981667   \n",
       "max              3.443000               2.250000               1.615840   \n",
       "\n",
       "       zaratio_Average  density_Average      Hardness  \n",
       "count     10407.000000     10407.000000  10407.000000  \n",
       "mean          0.493349         2.132984      4.647126  \n",
       "std           0.063080         1.936656      1.680525  \n",
       "min           0.000000         0.000000      1.000000  \n",
       "25%           0.476196         0.814800      3.000000  \n",
       "50%           0.488550         1.351550      5.500000  \n",
       "75%           0.496070         2.741550      6.000000  \n",
       "max           0.825990        10.970000     10.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a65fe631",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:53.568752Z",
     "iopub.status.busy": "2023-11-28T17:21:53.568354Z",
     "iopub.status.idle": "2023-11-28T17:21:53.598795Z",
     "shell.execute_reply": "2023-11-28T17:21:53.597183Z"
    },
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     "exception": false,
     "start_time": "2023-11-28T17:21:53.549009",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10407 entries, 0 to 10406\n",
      "Data columns (total 13 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   id                     10407 non-null  int64  \n",
      " 1   allelectrons_Total     10407 non-null  float64\n",
      " 2   density_Total          10407 non-null  float64\n",
      " 3   allelectrons_Average   10407 non-null  float64\n",
      " 4   val_e_Average          10407 non-null  float64\n",
      " 5   atomicweight_Average   10407 non-null  float64\n",
      " 6   ionenergy_Average      10407 non-null  float64\n",
      " 7   el_neg_chi_Average     10407 non-null  float64\n",
      " 8   R_vdw_element_Average  10407 non-null  float64\n",
      " 9   R_cov_element_Average  10407 non-null  float64\n",
      " 10  zaratio_Average        10407 non-null  float64\n",
      " 11  density_Average        10407 non-null  float64\n",
      " 12  Hardness               10407 non-null  float64\n",
      "dtypes: float64(12), int64(1)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b4628561",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:53.640119Z",
     "iopub.status.busy": "2023-11-28T17:21:53.638825Z",
     "iopub.status.idle": "2023-11-28T17:21:53.687548Z",
     "shell.execute_reply": "2023-11-28T17:21:53.686356Z"
    },
    "papermill": {
     "duration": 0.070389,
     "end_time": "2023-11-28T17:21:53.689910",
     "exception": false,
     "start_time": "2023-11-28T17:21:53.619521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>allelectrons_Total</th>\n",
       "      <th>density_Total</th>\n",
       "      <th>allelectrons_Average</th>\n",
       "      <th>val_e_Average</th>\n",
       "      <th>atomicweight_Average</th>\n",
       "      <th>ionenergy_Average</th>\n",
       "      <th>el_neg_chi_Average</th>\n",
       "      <th>R_vdw_element_Average</th>\n",
       "      <th>R_cov_element_Average</th>\n",
       "      <th>zaratio_Average</th>\n",
       "      <th>density_Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "      <td>6939.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13876.000000</td>\n",
       "      <td>126.460128</td>\n",
       "      <td>14.794020</td>\n",
       "      <td>17.406186</td>\n",
       "      <td>4.546852</td>\n",
       "      <td>38.422790</td>\n",
       "      <td>10.921512</td>\n",
       "      <td>2.608119</td>\n",
       "      <td>1.737907</td>\n",
       "      <td>0.949638</td>\n",
       "      <td>0.491675</td>\n",
       "      <td>2.152065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2003.261091</td>\n",
       "      <td>207.564499</td>\n",
       "      <td>18.982447</td>\n",
       "      <td>10.996089</td>\n",
       "      <td>0.683158</td>\n",
       "      <td>27.344351</td>\n",
       "      <td>1.378980</td>\n",
       "      <td>0.322873</td>\n",
       "      <td>0.190584</td>\n",
       "      <td>0.179266</td>\n",
       "      <td>0.060829</td>\n",
       "      <td>1.958213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10407.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12141.500000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>7.558488</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>20.298893</td>\n",
       "      <td>10.584314</td>\n",
       "      <td>2.527500</td>\n",
       "      <td>1.678000</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.476095</td>\n",
       "      <td>0.812440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13876.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.650000</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>26.203827</td>\n",
       "      <td>11.202760</td>\n",
       "      <td>2.706000</td>\n",
       "      <td>1.736000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.488550</td>\n",
       "      <td>1.351550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15610.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>16.601328</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>48.719500</td>\n",
       "      <td>11.645560</td>\n",
       "      <td>2.806667</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>0.496118</td>\n",
       "      <td>2.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17345.000000</td>\n",
       "      <td>10116.000000</td>\n",
       "      <td>643.093804</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>167.400000</td>\n",
       "      <td>15.245810</td>\n",
       "      <td>3.443000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.615333</td>\n",
       "      <td>0.825990</td>\n",
       "      <td>10.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  allelectrons_Total  density_Total  allelectrons_Average  \\\n",
       "count   6939.000000         6939.000000    6939.000000           6939.000000   \n",
       "mean   13876.000000          126.460128      14.794020             17.406186   \n",
       "std     2003.261091          207.564499      18.982447             10.996089   \n",
       "min    10407.000000            0.000000       0.000000              0.000000   \n",
       "25%    12141.500000           68.000000       7.558488             10.000000   \n",
       "50%    13876.000000          100.000000      10.650000             12.666667   \n",
       "75%    15610.500000          128.000000      16.601328             22.000000   \n",
       "max    17345.000000        10116.000000     643.093804             67.000000   \n",
       "\n",
       "       val_e_Average  atomicweight_Average  ionenergy_Average  \\\n",
       "count    6939.000000           6939.000000        6939.000000   \n",
       "mean        4.546852             38.422790          10.921512   \n",
       "std         0.683158             27.344351           1.378980   \n",
       "min         0.000000              0.000000           0.000000   \n",
       "25%         4.000000             20.298893          10.584314   \n",
       "50%         4.750000             26.203827          11.202760   \n",
       "75%         4.800000             48.719500          11.645560   \n",
       "max         6.000000            167.400000          15.245810   \n",
       "\n",
       "       el_neg_chi_Average  R_vdw_element_Average  R_cov_element_Average  \\\n",
       "count         6939.000000            6939.000000            6939.000000   \n",
       "mean             2.608119               1.737907               0.949638   \n",
       "std              0.322873               0.190584               0.179266   \n",
       "min              0.000000               0.000000               0.000000   \n",
       "25%              2.527500               1.678000               0.866667   \n",
       "50%              2.706000               1.736000               0.920000   \n",
       "75%              2.806667               1.820000               0.990000   \n",
       "max              3.443000               2.250000               1.615333   \n",
       "\n",
       "       zaratio_Average  density_Average  \n",
       "count      6939.000000      6939.000000  \n",
       "mean          0.491675         2.152065  \n",
       "std           0.060829         1.958213  \n",
       "min           0.000000         0.000000  \n",
       "25%           0.476095         0.812440  \n",
       "50%           0.488550         1.351550  \n",
       "75%           0.496118         2.780220  \n",
       "max           0.825990        10.970000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50689be3",
   "metadata": {
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     "duration": 0.018205,
     "end_time": "2023-11-28T17:21:53.788626",
     "exception": false,
     "start_time": "2023-11-28T17:21:53.770421",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "There are no missing values neither in `train` and `test` datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "975ab1cf",
   "metadata": {
    "papermill": {
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     "start_time": "2023-11-28T17:21:53.807185",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"4\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Data Exploration</h1>\n",
    "\n",
    "Notice that the data exploration is based on the competition dataset. First, we start by visualizing `Hardness`, the variable of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed1f168f",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:53.864718Z",
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     "start_time": "2023-11-28T17:21:53.843942",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data = train, x = 'Hardness', fill = True, color = 'steelblue');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f84503b",
   "metadata": {
    "papermill": {
     "duration": 0.018871,
     "end_time": "2023-11-28T17:21:54.389443",
     "exception": false,
     "start_time": "2023-11-28T17:21:54.370572",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The above plot shows `Hardness` is multi-modal. Next, we explore the relationship between the input features and `Hardness` by first creating a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "350c4e03",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:54.430058Z",
     "iopub.status.busy": "2023-11-28T17:21:54.429660Z",
     "iopub.status.idle": "2023-11-28T17:21:55.242869Z",
     "shell.execute_reply": "2023-11-28T17:21:55.241716Z"
    },
    "papermill": {
     "duration": 0.837593,
     "end_time": "2023-11-28T17:21:55.246476",
     "exception": false,
     "start_time": "2023-11-28T17:21:54.408883",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_mat_data = train.drop(columns = ['id'], axis = 1).corr()\n",
    "data_mask = np.triu(np.ones_like(corr_mat_data, dtype = bool))\n",
    "\n",
    "cmap = sns.diverging_palette(100, 7, s = 75, l = 40, n = 5, center = 'light', as_cmap = True)\n",
    "\n",
    "plt.figure(figsize = (12, 8))\n",
    "sns.heatmap(corr_mat_data, annot = True, cmap = cmap, fmt = '.2f', center = 0,\n",
    "            annot_kws = {'size': 12}, mask = data_mask).set_title('Correlations Among Features (in Competition Dataset)');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82630e64",
   "metadata": {
    "papermill": {
     "duration": 0.022236,
     "end_time": "2023-11-28T17:21:55.291457",
     "exception": false,
     "start_time": "2023-11-28T17:21:55.269221",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From the above, these are a couple of observations:\n",
    "\n",
    "- `atomicweight_Average` and `allelectrons_Average` are almost equal (99% correlation).\n",
    "- `R_cov_element_Average` and `R_vdw_element_Average` are highly correlated (81% correlation).\n",
    "- `density_Average` is highly correlated with `allelectrons_Average` and `atomicweight_Average` (80% correlation).\n",
    "- `allelectrons_Average` is the features with the highest correlation (in absolute value) with `Hardness`.\n",
    "\n",
    "Next, we take a close look at the scatter-plots of the highly correlated features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8f3dde0",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:55.338706Z",
     "iopub.status.busy": "2023-11-28T17:21:55.337722Z",
     "iopub.status.idle": "2023-11-28T17:21:55.978359Z",
     "shell.execute_reply": "2023-11-28T17:21:55.977128Z"
    },
    "papermill": {
     "duration": 0.667156,
     "end_time": "2023-11-28T17:21:55.981195",
     "exception": false,
     "start_time": "2023-11-28T17:21:55.314039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize = (15, 6))\n",
    "\n",
    "sns.scatterplot(ax = axes[0], data = train, x = 'atomicweight_Average', y = 'Hardness', color = 'steelblue')\n",
    "sns.scatterplot(ax = axes[1], data = train, x = 'density_Average', y = 'Hardness', color = 'steelblue');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1cf5f4",
   "metadata": {
    "papermill": {
     "duration": 0.024512,
     "end_time": "2023-11-28T17:21:56.031615",
     "exception": false,
     "start_time": "2023-11-28T17:21:56.007103",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In terms of patterns, not much to learnt from the above plots. However, \n",
    "\n",
    "- In the left panel, there are a few data points (on the bottom right) that seem to be outliers (in `atomicweight_Average`).\n",
    "- In the right panel, there are a few data points (on the right) that seem to be outliers (in `density_Average`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04638b49",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:56.086050Z",
     "iopub.status.busy": "2023-11-28T17:21:56.085676Z",
     "iopub.status.idle": "2023-11-28T17:21:58.940977Z",
     "shell.execute_reply": "2023-11-28T17:21:58.939545Z"
    },
    "papermill": {
     "duration": 2.888273,
     "end_time": "2023-11-28T17:21:58.947060",
     "exception": false,
     "start_time": "2023-11-28T17:21:56.058787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize = (15, 6))\n",
    "\n",
    "sns.scatterplot(ax = axes[0], data = train, x = 'R_cov_element_Average', y = 'R_vdw_element_Average', hue = 'Hardness')\n",
    "sns.scatterplot(ax = axes[1], data = train, x = 'density_Average', y = 'allelectrons_Average', hue = 'Hardness');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89357172",
   "metadata": {
    "papermill": {
     "duration": 0.029368,
     "end_time": "2023-11-28T17:21:59.006331",
     "exception": false,
     "start_time": "2023-11-28T17:21:58.976963",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "From the above plots, these are some observations:\n",
    "\n",
    "- In the left panel, there are several outliers.\n",
    "- In the right panel, it seems the top half of the plot (`allelectrosn_Average > 40`) `Hardness` is less than or equal to 4.5. \n",
    "- Also, in the right panel, there are several outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d572d885",
   "metadata": {
    "papermill": {
     "duration": 0.029314,
     "end_time": "2023-11-28T17:21:59.065517",
     "exception": false,
     "start_time": "2023-11-28T17:21:59.036203",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"5\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Baseline Modeling 1.0</h1>\n",
    "\n",
    "We start by modeling (using standard models) with raw data with minimum transformation. First, we define the input, target variables and the cross-validation folds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "497cf94d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:59.127150Z",
     "iopub.status.busy": "2023-11-28T17:21:59.126490Z",
     "iopub.status.idle": "2023-11-28T17:21:59.133275Z",
     "shell.execute_reply": "2023-11-28T17:21:59.132398Z"
    },
    "papermill": {
     "duration": 0.040356,
     "end_time": "2023-11-28T17:21:59.135517",
     "exception": false,
     "start_time": "2023-11-28T17:21:59.095161",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train.drop(columns = ['id', 'atomicweight_Average', 'Hardness'], axis = 1)\n",
    "Y = train['Hardness']\n",
    "\n",
    "skf = RepeatedKFold(n_splits = 10, n_repeats = 1, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24fd67a",
   "metadata": {
    "papermill": {
     "duration": 0.029851,
     "end_time": "2023-11-28T17:21:59.196042",
     "exception": false,
     "start_time": "2023-11-28T17:21:59.166191",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "First, we train the `KNeighborsRegressor` model with different number of neighbors as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "45ca01ae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:59.258229Z",
     "iopub.status.busy": "2023-11-28T17:21:59.257556Z",
     "iopub.status.idle": "2023-11-28T17:21:59.287734Z",
     "shell.execute_reply": "2023-11-28T17:21:59.286620Z"
    },
    "papermill": {
     "duration": 0.064129,
     "end_time": "2023-11-28T17:21:59.290153",
     "exception": false,
     "start_time": "2023-11-28T17:21:59.226024",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;pipeline&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;powertransformer&#x27;,\n",
       "                                                                   PowerTransformer())]),\n",
       "                                                  (&#x27;allelectrons_Total&#x27;,\n",
       "                                                   &#x27;density_Total&#x27;,\n",
       "                                                   &#x27;allelectrons_Average&#x27;,\n",
       "                                                   &#x27;val_e_Average&#x27;,\n",
       "                                                   &#x27;ionenergy_Average&#x27;,\n",
       "                                                   &#x27;el_neg_chi_Average&#x27;,\n",
       "                                                   &#x27;R_vdw_element_Average&#x27;,\n",
       "                                                   &#x27;R_cov_element_Average&#x27;,\n",
       "                                                   &#x27;zaratio_Average&#x27;,\n",
       "                                                   &#x27;density_Average&#x27;))])),\n",
       "                (&#x27;kneighborsregressor&#x27;, KNeighborsRegressor())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;pipeline&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;powertransformer&#x27;,\n",
       "                                                                   PowerTransformer())]),\n",
       "                                                  (&#x27;allelectrons_Total&#x27;,\n",
       "                                                   &#x27;density_Total&#x27;,\n",
       "                                                   &#x27;allelectrons_Average&#x27;,\n",
       "                                                   &#x27;val_e_Average&#x27;,\n",
       "                                                   &#x27;ionenergy_Average&#x27;,\n",
       "                                                   &#x27;el_neg_chi_Average&#x27;,\n",
       "                                                   &#x27;R_vdw_element_Average&#x27;,\n",
       "                                                   &#x27;R_cov_element_Average&#x27;,\n",
       "                                                   &#x27;zaratio_Average&#x27;,\n",
       "                                                   &#x27;density_Average&#x27;))])),\n",
       "                (&#x27;kneighborsregressor&#x27;, KNeighborsRegressor())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;pipeline&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;powertransformer&#x27;,\n",
       "                                                  PowerTransformer())]),\n",
       "                                 (&#x27;allelectrons_Total&#x27;, &#x27;density_Total&#x27;,\n",
       "                                  &#x27;allelectrons_Average&#x27;, &#x27;val_e_Average&#x27;,\n",
       "                                  &#x27;ionenergy_Average&#x27;, &#x27;el_neg_chi_Average&#x27;,\n",
       "                                  &#x27;R_vdw_element_Average&#x27;,\n",
       "                                  &#x27;R_cov_element_Average&#x27;, &#x27;zaratio_Average&#x27;,\n",
       "                                  &#x27;density_Average&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pipeline</label><div class=\"sk-toggleable__content\"><pre>(&#x27;allelectrons_Total&#x27;, &#x27;density_Total&#x27;, &#x27;allelectrons_Average&#x27;, &#x27;val_e_Average&#x27;, &#x27;ionenergy_Average&#x27;, &#x27;el_neg_chi_Average&#x27;, &#x27;R_vdw_element_Average&#x27;, &#x27;R_cov_element_Average&#x27;, &#x27;zaratio_Average&#x27;, &#x27;density_Average&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('pipeline',\n",
       "                                                  Pipeline(steps=[('powertransformer',\n",
       "                                                                   PowerTransformer())]),\n",
       "                                                  ('allelectrons_Total',\n",
       "                                                   'density_Total',\n",
       "                                                   'allelectrons_Average',\n",
       "                                                   'val_e_Average',\n",
       "                                                   'ionenergy_Average',\n",
       "                                                   'el_neg_chi_Average',\n",
       "                                                   'R_vdw_element_Average',\n",
       "                                                   'R_cov_element_Average',\n",
       "                                                   'zaratio_Average',\n",
       "                                                   'density_Average'))])),\n",
       "                ('kneighborsregressor', KNeighborsRegressor())])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand_tran = make_pipeline(PowerTransformer())\n",
    "# stand_tran = make_pipeline(RobustScaler())\n",
    "\n",
    "proccessor = make_column_transformer(\n",
    "    (stand_tran, ('allelectrons_Total', 'density_Total',\n",
    "                  'allelectrons_Average', 'val_e_Average',\n",
    "                  'ionenergy_Average', 'el_neg_chi_Average',\n",
    "                  'R_vdw_element_Average', 'R_cov_element_Average',\n",
    "                  'zaratio_Average', 'density_Average')), \n",
    "    remainder = 'passthrough')\n",
    "\n",
    "knn = make_pipeline(proccessor, KNeighborsRegressor())\n",
    "knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "962c8375",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:21:59.353093Z",
     "iopub.status.busy": "2023-11-28T17:21:59.352652Z",
     "iopub.status.idle": "2023-11-28T17:22:18.708976Z",
     "shell.execute_reply": "2023-11-28T17:22:18.707830Z"
    },
    "papermill": {
     "duration": 19.39075,
     "end_time": "2023-11-28T17:22:18.711393",
     "exception": false,
     "start_time": "2023-11-28T17:21:59.320643",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average oof Median absolure error score of the k-NN model with 5 neighbors is 0.7239999999999998\n",
      "The average oof Median absolure error score of the k-NN model with 10 neighbors is 0.7025000000000001\n",
      "The average oof Median absolure error score of the k-NN model with 15 neighbors is 0.7070000000000002\n",
      "The average oof Median absolure error score of the k-NN model with 20 neighbors is 0.7192499999999998\n",
      "The average oof Median absolure error score of the k-NN model with 25 neighbors is 0.7250000000000001\n",
      "The average oof Median absolure error score of the k-NN model with 30 neighbors is 0.7261666666666668\n",
      "The average oof Median absolure error score of the k-NN model with 35 neighbors is 0.7302142857142856\n",
      "The average oof Median absolure error score of the k-NN model with 40 neighbors is 0.7354375000000001\n",
      "The average oof Median absolure error score of the k-NN model with 45 neighbors is 0.7376111111111112\n",
      "The average oof Median absolure error score of the k-NN model with 50 neighbors is 0.7431500000000003\n",
      "The average oof Median absolure error score of the k-NN model with 70 neighbors is 0.7526428571428567\n",
      "The average oof Median absolure error score of the k-NN model with 100 neighbors is 0.7580000000000002\n",
      "The average oof Median absolure error score of the k-NN model with 150 neighbors is 0.7713500000000001\n",
      "The average oof Median absolure error score of the k-NN model with 200 neighbors is 0.7805249999999999\n"
     ]
    }
   ],
   "source": [
    "for i in [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 70, 100, 150, 200]:\n",
    "    \n",
    "    knn_cv_routine = cross_val_score(make_pipeline(proccessor, KNeighborsRegressor(n_neighbors = i)), X, Y,\n",
    "                                     scoring = 'neg_median_absolute_error',\n",
    "                                     cv = skf,\n",
    "                                     n_jobs = -1)\n",
    "\n",
    "    print(f\"The average oof Median absolure error score of the k-NN model with {i} neighbors is {-1*knn_cv_routine.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7879bdb6",
   "metadata": {
    "papermill": {
     "duration": 0.03099,
     "end_time": "2023-11-28T17:22:18.773424",
     "exception": false,
     "start_time": "2023-11-28T17:22:18.742434",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Ten neighbors seems the be appropriate number of neighbors for this dataset. Next, we proceed to train `RandomForestRegressor` on the `train` dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5cb129",
   "metadata": {
    "papermill": {
     "duration": 0.031043,
     "end_time": "2023-11-28T17:22:18.835194",
     "exception": false,
     "start_time": "2023-11-28T17:22:18.804151",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we proceed to train the `GradientBoostingRegressor` model on the `train` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5ee38be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:22:18.900369Z",
     "iopub.status.busy": "2023-11-28T17:22:18.899671Z",
     "iopub.status.idle": "2023-11-28T17:22:58.660462Z",
     "shell.execute_reply": "2023-11-28T17:22:58.659090Z"
    },
    "papermill": {
     "duration": 39.82319,
     "end_time": "2023-11-28T17:22:58.690247",
     "exception": false,
     "start_time": "2023-11-28T17:22:18.867057",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average oof Median absolure error score of the GB model is 0.5337928560757206\n"
     ]
    }
   ],
   "source": [
    "GB_cv_routine = cross_val_score(GradientBoostingRegressor(loss = 'absolute_error',\n",
    "                                                          n_estimators = 300,\n",
    "                                                          learning_rate = 0.1,\n",
    "                                                          min_samples_leaf = 30,\n",
    "                                                          max_depth = 5), \n",
    "                                X,\n",
    "                                Y,\n",
    "                                scoring = 'neg_median_absolute_error',\n",
    "                                cv = skf, \n",
    "                                n_jobs = -1)\n",
    "\n",
    "print(f\"The average oof Median absolure error score of the GB model is {-1*GB_cv_routine.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e87e36",
   "metadata": {
    "papermill": {
     "duration": 0.030603,
     "end_time": "2023-11-28T17:22:58.753281",
     "exception": false,
     "start_time": "2023-11-28T17:22:58.722678",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we proceed to train the `HistGradientBoostingRegressor` model on the `train` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f525a9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:22:58.818067Z",
     "iopub.status.busy": "2023-11-28T17:22:58.816957Z",
     "iopub.status.idle": "2023-11-28T17:23:04.453104Z",
     "shell.execute_reply": "2023-11-28T17:23:04.452000Z"
    },
    "papermill": {
     "duration": 5.671718,
     "end_time": "2023-11-28T17:23:04.455877",
     "exception": false,
     "start_time": "2023-11-28T17:22:58.784159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average oof Median absolure error score of the HistGB model is 0.5401412730035817\n"
     ]
    }
   ],
   "source": [
    "HistGB_cv_routine = cross_val_score(HistGradientBoostingRegressor(loss = 'absolute_error',\n",
    "                                                                  max_iter = 300,\n",
    "                                                                  learning_rate = 0.1,\n",
    "                                                                  min_samples_leaf = 30,\n",
    "                                                                  max_depth = 5, \n",
    "                                                                  random_state = 1), \n",
    "                                    X,\n",
    "                                    Y,\n",
    "                                    scoring = 'neg_median_absolute_error',\n",
    "                                    cv = skf, \n",
    "                                    n_jobs = -1)\n",
    "\n",
    "print(f\"The average oof Median absolure error score of the HistGB model is {-1*HistGB_cv_routine.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a50f22e",
   "metadata": {
    "papermill": {
     "duration": 0.031851,
     "end_time": "2023-11-28T17:23:04.518334",
     "exception": false,
     "start_time": "2023-11-28T17:23:04.486483",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we proceed to train the `LGBRegressor` model on the `train` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ad686e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:23:04.582496Z",
     "iopub.status.busy": "2023-11-28T17:23:04.582029Z",
     "iopub.status.idle": "2023-11-28T17:23:11.869818Z",
     "shell.execute_reply": "2023-11-28T17:23:11.868552Z"
    },
    "papermill": {
     "duration": 7.323611,
     "end_time": "2023-11-28T17:23:11.872381",
     "exception": false,
     "start_time": "2023-11-28T17:23:04.548770",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average oof Median absolure error score of the LGBM model is 0.5302365278001064\n"
     ]
    }
   ],
   "source": [
    "LGBM_cv_routine = cross_val_score(LGBMRegressor(objective = 'mae',\n",
    "                                                n_estimators = 300,\n",
    "                                                learning_rate = 0.1,\n",
    "                                                colsample_bytree = 0.6), \n",
    "                                  X,\n",
    "                                  Y,\n",
    "                                  scoring = 'neg_median_absolute_error',\n",
    "                                  cv = skf, \n",
    "                                  n_jobs = -1)\n",
    "\n",
    "print(f\"The average oof Median absolure error score of the LGBM model is {-1*LGBM_cv_routine.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f9849f3",
   "metadata": {
    "papermill": {
     "duration": 0.030829,
     "end_time": "2023-11-28T17:23:11.936110",
     "exception": false,
     "start_time": "2023-11-28T17:23:11.905281",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we proceed to train the `XGBRegressor` model on the `train` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3281c2b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:23:12.013150Z",
     "iopub.status.busy": "2023-11-28T17:23:12.012743Z",
     "iopub.status.idle": "2023-11-28T17:23:26.409680Z",
     "shell.execute_reply": "2023-11-28T17:23:26.407845Z"
    },
    "papermill": {
     "duration": 14.438196,
     "end_time": "2023-11-28T17:23:26.412174",
     "exception": false,
     "start_time": "2023-11-28T17:23:11.973978",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average oof Median absolure error score of the XGB model is 0.5350464749336241\n"
     ]
    }
   ],
   "source": [
    "XGB_cv_routine = cross_val_score(XGBRegressor(objective = 'reg:absoluteerror',\n",
    "                                              tree_method = 'hist',\n",
    "                                              n_estimators = 300,\n",
    "                                              learning_rate = 0.1,\n",
    "                                              colsample_bytree = 0.6), \n",
    "                                 X,\n",
    "                                 Y,\n",
    "                                 scoring = 'neg_median_absolute_error',\n",
    "                                 cv = skf, \n",
    "                                 n_jobs = -1)\n",
    "\n",
    "print(f\"The average oof Median absolure error score of the XGB model is {-1*XGB_cv_routine.mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d629191a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:23:26.477540Z",
     "iopub.status.busy": "2023-11-28T17:23:26.477104Z",
     "iopub.status.idle": "2023-11-28T17:23:41.913038Z",
     "shell.execute_reply": "2023-11-28T17:23:41.912009Z"
    },
    "papermill": {
     "duration": 15.471181,
     "end_time": "2023-11-28T17:23:41.916146",
     "exception": false,
     "start_time": "2023-11-28T17:23:26.444965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average oof Median absolure error score of the CatBoost model is 0.5518818906488897\n"
     ]
    }
   ],
   "source": [
    "Cat_cv_routine = cross_val_score(CatBoostRegressor(objective = 'MAE',\n",
    "                                                   iterations = 300,\n",
    "                                                   learning_rate = 0.1,\n",
    "                                                   verbose = False), \n",
    "                                 X,\n",
    "                                 Y,\n",
    "                                 scoring = 'neg_median_absolute_error',\n",
    "                                 cv = skf, \n",
    "                                 n_jobs = -1)\n",
    "\n",
    "print(f\"The average oof Median absolure error score of the CatBoost model is {-1*Cat_cv_routine.mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aef8b101",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:23:41.992798Z",
     "iopub.status.busy": "2023-11-28T17:23:41.991106Z",
     "iopub.status.idle": "2023-11-28T17:23:42.008405Z",
     "shell.execute_reply": "2023-11-28T17:23:42.007133Z"
    },
    "papermill": {
     "duration": 0.063588,
     "end_time": "2023-11-28T17:23:42.012125",
     "exception": false,
     "start_time": "2023-11-28T17:23:41.948537",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The followig table shows the performance of the considered models: \n",
      "\n",
      "  Model  10-folds oof Median Absolute Error\n",
      "0    GB                            0.533793\n",
      "1  Hist                            0.540141\n",
      "2  LGBM                            0.530237\n",
      "3   XGB                            0.535046\n",
      "4   Cat                            0.551882\n"
     ]
    }
   ],
   "source": [
    "model_performance = pd.DataFrame()\n",
    "model_performance['Model'] = ['GB', 'Hist', 'LGBM', 'XGB', 'Cat']\n",
    "model_performance['10-folds oof Median Absolute Error'] = [-1*GB_cv_routine.mean(), -1*HistGB_cv_routine.mean(), -1*LGBM_cv_routine.mean(), -1*XGB_cv_routine.mean(), -1*Cat_cv_routine.mean()]\n",
    "print(f\"The followig table shows the performance of the considered models: \\n\\n{model_performance}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff7d54b",
   "metadata": {
    "papermill": {
     "duration": 0.032735,
     "end_time": "2023-11-28T17:23:42.086843",
     "exception": false,
     "start_time": "2023-11-28T17:23:42.054108",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we build two model: `VotingRegressor` and `StackerRegressor` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2f039e05",
   "metadata": {
    "_kg_hide-input": true,
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     "iopub.status.idle": "2023-11-28T17:23:42.261705Z",
     "shell.execute_reply": "2023-11-28T17:23:42.260631Z"
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     "start_time": "2023-11-28T17:23:42.119624",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingRegressor(estimators=[(&#x27;GB&#x27;,\n",
       "                             Pipeline(steps=[(&#x27;powertransformer&#x27;,\n",
       "                                              PowerTransformer()),\n",
       "                                             (&#x27;gradientboostingregressor&#x27;,\n",
       "                                              GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                                                                        loss=&#x27;absolute_error&#x27;,\n",
       "                                                                        max_depth=10,\n",
       "                                                                        min_samples_leaf=42,\n",
       "                                                                        min_samples_split=11,\n",
       "                                                                        n_estimators=139,\n",
       "                                                                        random_state=1))])),\n",
       "                            (&#x27;HGB&#x27;,\n",
       "                             Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),\n",
       "                                             (&#x27;...\n",
       "                                                           max_cat_to_onehot=None,\n",
       "                                                           max_delta_step=None,\n",
       "                                                           max_depth=7,\n",
       "                                                           max_leaves=None,\n",
       "                                                           min_child_weight=28,\n",
       "                                                           missing=nan,\n",
       "                                                           monotone_constraints=None,\n",
       "                                                           multi_strategy=None,\n",
       "                                                           n_estimators=960,\n",
       "                                                           n_jobs=None,\n",
       "                                                           num_parallel_tree=None,\n",
       "                                                           objective=&#x27;reg:absoluteerror&#x27;, ...))])),\n",
       "                            (&#x27;Cat&#x27;,\n",
       "                             &lt;catboost.core.CatBoostRegressor object at 0x79d81c5635b0&gt;)],\n",
       "                n_jobs=-1,\n",
       "                weights=[0.002309, 0.254678, 0.363684, 0.300134, 0.074709])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingRegressor</label><div class=\"sk-toggleable__content\"><pre>VotingRegressor(estimators=[(&#x27;GB&#x27;,\n",
       "                             Pipeline(steps=[(&#x27;powertransformer&#x27;,\n",
       "                                              PowerTransformer()),\n",
       "                                             (&#x27;gradientboostingregressor&#x27;,\n",
       "                                              GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                                                                        loss=&#x27;absolute_error&#x27;,\n",
       "                                                                        max_depth=10,\n",
       "                                                                        min_samples_leaf=42,\n",
       "                                                                        min_samples_split=11,\n",
       "                                                                        n_estimators=139,\n",
       "                                                                        random_state=1))])),\n",
       "                            (&#x27;HGB&#x27;,\n",
       "                             Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),\n",
       "                                             (&#x27;...\n",
       "                                                           max_cat_to_onehot=None,\n",
       "                                                           max_delta_step=None,\n",
       "                                                           max_depth=7,\n",
       "                                                           max_leaves=None,\n",
       "                                                           min_child_weight=28,\n",
       "                                                           missing=nan,\n",
       "                                                           monotone_constraints=None,\n",
       "                                                           multi_strategy=None,\n",
       "                                                           n_estimators=960,\n",
       "                                                           n_jobs=None,\n",
       "                                                           num_parallel_tree=None,\n",
       "                                                           objective=&#x27;reg:absoluteerror&#x27;, ...))])),\n",
       "                            (&#x27;Cat&#x27;,\n",
       "                             &lt;catboost.core.CatBoostRegressor object at 0x79d81c5635b0&gt;)],\n",
       "                n_jobs=-1,\n",
       "                weights=[0.002309, 0.254678, 0.363684, 0.300134, 0.074709])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>GB</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                          loss=&#x27;absolute_error&#x27;, max_depth=10,\n",
       "                          min_samples_leaf=42, min_samples_split=11,\n",
       "                          n_estimators=139, random_state=1)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>HGB</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HistGradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingRegressor(early_stopping=False,\n",
       "                              l2_regularization=0.027030940923710774,\n",
       "                              learning_rate=0.019042088959167168,\n",
       "                              loss=&#x27;absolute_error&#x27;, max_depth=14, max_iter=949,\n",
       "                              max_leaf_nodes=50, min_samples_leaf=50)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>LGBM</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(colsample_bytree=0.7716446660869791,\n",
       "              learning_rate=0.018499866546319983, max_depth=11,\n",
       "              n_estimators=668, num_leaves=88, objective=&#x27;mae&#x27;,\n",
       "              reg_alpha=0.4618095706853164, reg_lambda=0.07505699333277592,\n",
       "              subsample=0.7994357898443023)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>XGB</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.6171930281823468, device=None,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=1.3282085968831892,\n",
       "             grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.01912673399861771,\n",
       "             max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=28, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=960, n_jobs=None,\n",
       "             num_parallel_tree=None, objective=&#x27;reg:absoluteerror&#x27;, ...)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>Cat</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x79d81c5635b0&gt;</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingRegressor(estimators=[('GB',\n",
       "                             Pipeline(steps=[('powertransformer',\n",
       "                                              PowerTransformer()),\n",
       "                                             ('gradientboostingregressor',\n",
       "                                              GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                                                                        loss='absolute_error',\n",
       "                                                                        max_depth=10,\n",
       "                                                                        min_samples_leaf=42,\n",
       "                                                                        min_samples_split=11,\n",
       "                                                                        n_estimators=139,\n",
       "                                                                        random_state=1))])),\n",
       "                            ('HGB',\n",
       "                             Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
       "                                             ('...\n",
       "                                                           max_cat_to_onehot=None,\n",
       "                                                           max_delta_step=None,\n",
       "                                                           max_depth=7,\n",
       "                                                           max_leaves=None,\n",
       "                                                           min_child_weight=28,\n",
       "                                                           missing=nan,\n",
       "                                                           monotone_constraints=None,\n",
       "                                                           multi_strategy=None,\n",
       "                                                           n_estimators=960,\n",
       "                                                           n_jobs=None,\n",
       "                                                           num_parallel_tree=None,\n",
       "                                                           objective='reg:absoluteerror', ...))])),\n",
       "                            ('Cat',\n",
       "                             <catboost.core.CatBoostRegressor object at 0x79d81c5635b0>)],\n",
       "                n_jobs=-1,\n",
       "                weights=[0.002309, 0.254678, 0.363684, 0.300134, 0.074709])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md1 = make_pipeline(PowerTransformer(), GradientBoostingRegressor(**{'loss': 'absolute_error',\n",
    " 'max_depth': 10,\n",
    " 'learning_rate': 0.041599576923587865,\n",
    " 'n_estimators': 139,\n",
    " 'min_samples_leaf': 42,\n",
    " 'min_samples_split': 11,\n",
    " 'random_state': 1}))\n",
    "\n",
    "md2 = make_pipeline(MinMaxScaler(), HistGradientBoostingRegressor(**{'loss': 'absolute_error',\n",
    " 'l2_regularization': 0.027030940923710774,\n",
    " 'early_stopping': False,\n",
    " 'learning_rate': 0.019042088959167168,\n",
    " 'max_iter': 949,\n",
    " 'max_depth': 14,\n",
    " 'max_bins': 255,\n",
    " 'min_samples_leaf': 50,\n",
    " 'max_leaf_nodes': 50}))\n",
    "\n",
    "md3 = LGBMRegressor(**{'objective': 'mae',\n",
    " 'n_estimators': 668,\n",
    " 'learning_rate': 0.018499866546319983,\n",
    " 'max_depth': 11,\n",
    " 'reg_alpha': 0.4618095706853164,\n",
    " 'reg_lambda': 0.07505699333277592,\n",
    " 'num_leaves': 88,\n",
    " 'subsample': 0.7994357898443023,\n",
    " 'colsample_bytree': 0.7716446660869791})\n",
    "\n",
    "md4 = make_pipeline(MinMaxScaler(), XGBRegressor(**{'objective': 'reg:absoluteerror',\n",
    " 'tree_method': 'hist',\n",
    " 'max_depth': 7,\n",
    " 'learning_rate': 0.01912673399861771,\n",
    " 'n_estimators': 960,\n",
    " 'gamma': 1.3282085968831892,\n",
    " 'min_child_weight': 28,\n",
    " 'colsample_bytree': 0.6171930281823468,\n",
    " 'subsample': 0.762767668956589}))\n",
    "\n",
    "md5 = CatBoostRegressor(**{'objective': 'MAE',\n",
    " 'iterations': 792,\n",
    " 'learning_rate': 0.033323612065351636,\n",
    " 'depth': 7,\n",
    " 'random_strength': 0.04415624028064764,\n",
    " 'bagging_temperature': 0.5522406534278442,\n",
    " 'border_count': 241,\n",
    " 'l2_leaf_reg': 8,\n",
    " 'verbose': False,\n",
    " 'task_type': 'CPU'})\n",
    "\n",
    "md6 = make_pipeline(PowerTransformer(), SVR(kernel = 'rbf', \n",
    "                                            C = 10,\n",
    "                                            gamma = 0.1, \n",
    "                                            epsilon = 0.01))\n",
    "\n",
    "voting_regressor = VotingRegressor(estimators = [('GB', md1),\n",
    "                                                 ('HGB', md2),\n",
    "                                                 ('LGBM', md3),\n",
    "                                                 ('XGB', md4),\n",
    "                                                 ('Cat', md5)],\n",
    "                                  n_jobs = -1, \n",
    "                                  weights = [0.002309, 0.254678, 0.363684, 0.300134, 0.074709])\n",
    "voting_regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2a68cc2d",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T17:23:42.331258Z",
     "iopub.status.busy": "2023-11-28T17:23:42.330819Z",
     "iopub.status.idle": "2023-11-28T17:23:42.429375Z",
     "shell.execute_reply": "2023-11-28T17:23:42.428318Z"
    },
    "papermill": {
     "duration": 0.134802,
     "end_time": "2023-11-28T17:23:42.431581",
     "exception": false,
     "start_time": "2023-11-28T17:23:42.296779",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingRegressor(estimators=[(&#x27;GB&#x27;,\n",
       "                               Pipeline(steps=[(&#x27;powertransformer&#x27;,\n",
       "                                                PowerTransformer()),\n",
       "                                               (&#x27;gradientboostingregressor&#x27;,\n",
       "                                                GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                                                                          loss=&#x27;absolute_error&#x27;,\n",
       "                                                                          max_depth=10,\n",
       "                                                                          min_samples_leaf=42,\n",
       "                                                                          min_samples_split=11,\n",
       "                                                                          n_estimators=139,\n",
       "                                                                          random_state=1))])),\n",
       "                              (&#x27;HGB&#x27;,\n",
       "                               Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),...\n",
       "                                                             max_cat_threshold=None,\n",
       "                                                             max_cat_to_onehot=None,\n",
       "                                                             max_delta_step=None,\n",
       "                                                             max_depth=7,\n",
       "                                                             max_leaves=None,\n",
       "                                                             min_child_weight=28,\n",
       "                                                             missing=nan,\n",
       "                                                             monotone_constraints=None,\n",
       "                                                             multi_strategy=None,\n",
       "                                                             n_estimators=960,\n",
       "                                                             n_jobs=None,\n",
       "                                                             num_parallel_tree=None,\n",
       "                                                             objective=&#x27;reg:absoluteerror&#x27;, ...))])),\n",
       "                              (&#x27;Cat&#x27;,\n",
       "                               &lt;catboost.core.CatBoostRegressor object at 0x79d81c5635b0&gt;)],\n",
       "                  final_estimator=LADRegression(), n_jobs=-1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingRegressor</label><div class=\"sk-toggleable__content\"><pre>StackingRegressor(estimators=[(&#x27;GB&#x27;,\n",
       "                               Pipeline(steps=[(&#x27;powertransformer&#x27;,\n",
       "                                                PowerTransformer()),\n",
       "                                               (&#x27;gradientboostingregressor&#x27;,\n",
       "                                                GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                                                                          loss=&#x27;absolute_error&#x27;,\n",
       "                                                                          max_depth=10,\n",
       "                                                                          min_samples_leaf=42,\n",
       "                                                                          min_samples_split=11,\n",
       "                                                                          n_estimators=139,\n",
       "                                                                          random_state=1))])),\n",
       "                              (&#x27;HGB&#x27;,\n",
       "                               Pipeline(steps=[(&#x27;minmaxscaler&#x27;, MinMaxScaler()),...\n",
       "                                                             max_cat_threshold=None,\n",
       "                                                             max_cat_to_onehot=None,\n",
       "                                                             max_delta_step=None,\n",
       "                                                             max_depth=7,\n",
       "                                                             max_leaves=None,\n",
       "                                                             min_child_weight=28,\n",
       "                                                             missing=nan,\n",
       "                                                             monotone_constraints=None,\n",
       "                                                             multi_strategy=None,\n",
       "                                                             n_estimators=960,\n",
       "                                                             n_jobs=None,\n",
       "                                                             num_parallel_tree=None,\n",
       "                                                             objective=&#x27;reg:absoluteerror&#x27;, ...))])),\n",
       "                              (&#x27;Cat&#x27;,\n",
       "                               &lt;catboost.core.CatBoostRegressor object at 0x79d81c5635b0&gt;)],\n",
       "                  final_estimator=LADRegression(), n_jobs=-1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>GB</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                          loss=&#x27;absolute_error&#x27;, max_depth=10,\n",
       "                          min_samples_leaf=42, min_samples_split=11,\n",
       "                          n_estimators=139, random_state=1)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>HGB</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HistGradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingRegressor(early_stopping=False,\n",
       "                              l2_regularization=0.027030940923710774,\n",
       "                              learning_rate=0.019042088959167168,\n",
       "                              loss=&#x27;absolute_error&#x27;, max_depth=14, max_iter=949,\n",
       "                              max_leaf_nodes=50, min_samples_leaf=50)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>LGBM</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(colsample_bytree=0.7716446660869791,\n",
       "              learning_rate=0.018499866546319983, max_depth=11,\n",
       "              n_estimators=668, num_leaves=88, objective=&#x27;mae&#x27;,\n",
       "              reg_alpha=0.4618095706853164, reg_lambda=0.07505699333277592,\n",
       "              subsample=0.7994357898443023)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>XGB</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.6171930281823468, device=None,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, feature_types=None, gamma=1.3282085968831892,\n",
       "             grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.01912673399861771,\n",
       "             max_bin=None, max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "             min_child_weight=28, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=960, n_jobs=None,\n",
       "             num_parallel_tree=None, objective=&#x27;reg:absoluteerror&#x27;, ...)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>Cat</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x79d81c5635b0&gt;</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LADRegression</label><div class=\"sk-toggleable__content\"><pre>LADRegression()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "StackingRegressor(estimators=[('GB',\n",
       "                               Pipeline(steps=[('powertransformer',\n",
       "                                                PowerTransformer()),\n",
       "                                               ('gradientboostingregressor',\n",
       "                                                GradientBoostingRegressor(learning_rate=0.041599576923587865,\n",
       "                                                                          loss='absolute_error',\n",
       "                                                                          max_depth=10,\n",
       "                                                                          min_samples_leaf=42,\n",
       "                                                                          min_samples_split=11,\n",
       "                                                                          n_estimators=139,\n",
       "                                                                          random_state=1))])),\n",
       "                              ('HGB',\n",
       "                               Pipeline(steps=[('minmaxscaler', MinMaxScaler()),...\n",
       "                                                             max_cat_threshold=None,\n",
       "                                                             max_cat_to_onehot=None,\n",
       "                                                             max_delta_step=None,\n",
       "                                                             max_depth=7,\n",
       "                                                             max_leaves=None,\n",
       "                                                             min_child_weight=28,\n",
       "                                                             missing=nan,\n",
       "                                                             monotone_constraints=None,\n",
       "                                                             multi_strategy=None,\n",
       "                                                             n_estimators=960,\n",
       "                                                             n_jobs=None,\n",
       "                                                             num_parallel_tree=None,\n",
       "                                                             objective='reg:absoluteerror', ...))])),\n",
       "                              ('Cat',\n",
       "                               <catboost.core.CatBoostRegressor object at 0x79d81c5635b0>)],\n",
       "                  final_estimator=LADRegression(), n_jobs=-1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacker = StackingRegressor(estimators = [('GB', md1),\n",
    "                                          ('HGB', md2),\n",
    "                                          ('LGBM', md3),\n",
    "                                          ('XGB', md4),\n",
    "                                          ('Cat', md5)],\n",
    "                            n_jobs = -1, \n",
    "                            final_estimator = LADRegression())\n",
    "stacker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3d2680",
   "metadata": {
    "papermill": {
     "duration": 0.033299,
     "end_time": "2023-11-28T17:23:42.496903",
     "exception": false,
     "start_time": "2023-11-28T17:23:42.463604",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we proceed to build the cross-validation routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "675635eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T17:23:42.566389Z",
     "iopub.status.busy": "2023-11-28T17:23:42.565933Z",
     "iopub.status.idle": "2023-11-28T18:03:14.427319Z",
     "shell.execute_reply": "2023-11-28T18:03:14.424251Z"
    },
    "papermill": {
     "duration": 2371.901263,
     "end_time": "2023-11-28T18:03:14.431133",
     "exception": false,
     "start_time": "2023-11-28T17:23:42.529870",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------\n",
      "Fold 0 ==> Voting Regressor oof median absolute error score is ==> 0.49609987271717415\n",
      "Fold 0 ==> Stacking Regressor oof median absolute error score is ==> 0.507646895955701\n",
      "----------------------------------------------------------\n",
      "Fold 1 ==> Voting Regressor oof median absolute error score is ==> 0.5120088203646027\n",
      "Fold 1 ==> Stacking Regressor oof median absolute error score is ==> 0.5170554130431677\n",
      "----------------------------------------------------------\n",
      "Fold 2 ==> Voting Regressor oof median absolute error score is ==> 0.5017178350490852\n",
      "Fold 2 ==> Stacking Regressor oof median absolute error score is ==> 0.49423183732711085\n",
      "----------------------------------------------------------\n",
      "Fold 3 ==> Voting Regressor oof median absolute error score is ==> 0.518964555854311\n",
      "Fold 3 ==> Stacking Regressor oof median absolute error score is ==> 0.5217407028242738\n",
      "----------------------------------------------------------\n",
      "Fold 4 ==> Voting Regressor oof median absolute error score is ==> 0.5332942611530216\n",
      "Fold 4 ==> Stacking Regressor oof median absolute error score is ==> 0.5338482706469343\n",
      "----------------------------------------------------------\n",
      "Fold 5 ==> Voting Regressor oof median absolute error score is ==> 0.5366089591345844\n",
      "Fold 5 ==> Stacking Regressor oof median absolute error score is ==> 0.5254736747197635\n",
      "----------------------------------------------------------\n",
      "Fold 6 ==> Voting Regressor oof median absolute error score is ==> 0.6024459331843364\n",
      "Fold 6 ==> Stacking Regressor oof median absolute error score is ==> 0.6080563480281107\n",
      "----------------------------------------------------------\n",
      "Fold 7 ==> Voting Regressor oof median absolute error score is ==> 0.5134594612196801\n",
      "Fold 7 ==> Stacking Regressor oof median absolute error score is ==> 0.5083769592048171\n",
      "----------------------------------------------------------\n",
      "Fold 8 ==> Voting Regressor oof median absolute error score is ==> 0.4876072418938766\n",
      "Fold 8 ==> Stacking Regressor oof median absolute error score is ==> 0.4834040518633458\n",
      "----------------------------------------------------------\n",
      "Fold 9 ==> Voting Regressor oof median absolute error score is ==> 0.500272014210243\n",
      "Fold 9 ==> Stacking Regressor oof median absolute error score is ==> 0.5020361862260474\n"
     ]
    }
   ],
   "source": [
    "vot_scores, vot_preds = list(), list()\n",
    "stack_scores, stack_preds = list(), list()\n",
    "\n",
    "for i, (train_idx, test_idx) in enumerate(skf.split(X, Y)):\n",
    "\n",
    "    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
    "    Y_train, Y_test = Y.iloc[train_idx], Y.iloc[test_idx]\n",
    "    \n",
    "    print('----------------------------------------------------------')\n",
    "    \n",
    "    ############\n",
    "    ## Voting ##\n",
    "    ############\n",
    "    \n",
    "    vot = voting_regressor.fit(X_train, Y_train)\n",
    "    vot_pred = vot.predict(X_test)\n",
    "    \n",
    "    vot_score = median_absolute_error(Y_test, vot_pred)\n",
    "    vot_scores.append(vot_score)\n",
    "    \n",
    "    print('Fold', i, '==> Voting Regressor oof median absolute error score is ==>', vot_score)\n",
    "    \n",
    "    vot_pred_test = vot.predict(test.drop(columns = ['id', 'atomicweight_Average'], axis = 1))\n",
    "    vot_preds.append(vot_pred_test)\n",
    "    \n",
    "    #############\n",
    "    ## Stacker ##\n",
    "    #############\n",
    "    \n",
    "    stack = stacker.fit(X_train, Y_train)\n",
    "    stack_pred = stack.predict(X_test)\n",
    "    \n",
    "    stack_score = median_absolute_error(Y_test, stack_pred)\n",
    "    stack_scores.append(stack_score)\n",
    "    \n",
    "    print('Fold', i, '==> Stacking Regressor oof median absolute error score is ==>', stack_score)\n",
    "    \n",
    "    stack_pred_test = stack.predict(test.drop(columns = ['id', 'atomicweight_Average'], axis = 1))\n",
    "    stack_preds.append(stack_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "922f7193",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:03:14.508166Z",
     "iopub.status.busy": "2023-11-28T18:03:14.507759Z",
     "iopub.status.idle": "2023-11-28T18:03:14.513739Z",
     "shell.execute_reply": "2023-11-28T18:03:14.512713Z"
    },
    "papermill": {
     "duration": 0.044193,
     "end_time": "2023-11-28T18:03:14.516273",
     "exception": false,
     "start_time": "2023-11-28T18:03:14.472080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average oof median absolute error score of voting regressor model is 0.5202478954780915\n",
      "The average oof median absolute error score of stacking regressor model is 0.5201870339839272\n"
     ]
    }
   ],
   "source": [
    "print(f\"The average oof median absolute error score of voting regressor model is {np.mean(vot_scores)}\")\n",
    "print(f\"The average oof median absolute error score of stacking regressor model is {np.mean(stack_scores)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0c3900e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:03:14.588054Z",
     "iopub.status.busy": "2023-11-28T18:03:14.587609Z",
     "iopub.status.idle": "2023-11-28T18:03:54.207457Z",
     "shell.execute_reply": "2023-11-28T18:03:54.206062Z"
    },
    "papermill": {
     "duration": 39.689361,
     "end_time": "2023-11-28T18:03:54.240032",
     "exception": false,
     "start_time": "2023-11-28T18:03:14.550671",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10407</td>\n",
       "      <td>2.615389</td>\n",
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       "      <td>2.548962</td>\n",
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       "      <td>10409</td>\n",
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       "      <th>3</th>\n",
       "      <td>10410</td>\n",
       "      <td>4.308342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10411</td>\n",
       "      <td>5.355002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      id  Hardness\n",
       "0  10407  2.615389\n",
       "1  10408  2.548962\n",
       "2  10409  6.008095\n",
       "3  10410  4.308342\n",
       "4  10411  5.355002"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_full = voting_regressor.fit(X, Y)\n",
    "\n",
    "voting_pred_full = voting_full.predict(test.drop(columns = ['id', 'atomicweight_Average'], axis = 1))\n",
    "voting_pred_cv = pd.DataFrame(vot_preds).apply(np.median, axis = 0)\n",
    "\n",
    "voting = (voting_pred_full + voting_pred_cv) / 2\n",
    "submission['Hardness'] = voting\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0a65aab8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:03:54.310218Z",
     "iopub.status.busy": "2023-11-28T18:03:54.309262Z",
     "iopub.status.idle": "2023-11-28T18:03:54.337403Z",
     "shell.execute_reply": "2023-11-28T18:03:54.336147Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv('Voting_baseline_sub.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a9ca4bbd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:03:54.411172Z",
     "iopub.status.busy": "2023-11-28T18:03:54.410235Z",
     "iopub.status.idle": "2023-11-28T18:07:27.603741Z",
     "shell.execute_reply": "2023-11-28T18:07:27.602446Z"
    },
    "papermill": {
     "duration": 213.255547,
     "end_time": "2023-11-28T18:07:27.630242",
     "exception": false,
     "start_time": "2023-11-28T18:03:54.374695",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
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      "text/plain": [
       "      id  Hardness\n",
       "0  10407  2.569433\n",
       "1  10408  2.540628\n",
       "2  10409  6.025345\n",
       "3  10410  4.319341\n",
       "4  10411  5.402557"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacker_full = stacker.fit(X, Y)\n",
    "\n",
    "stacker_pred_full = stacker_full.predict(test.drop(columns = ['id', 'atomicweight_Average'], axis = 1))\n",
    "stacker_pred_cv = pd.DataFrame(stack_preds).apply(np.median, axis = 0)\n",
    "\n",
    "stacking = (stacker_pred_full + stacker_pred_cv) / 2\n",
    "submission['Hardness'] = stacking\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e38c775",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:07:27.701033Z",
     "iopub.status.busy": "2023-11-28T18:07:27.700556Z",
     "iopub.status.idle": "2023-11-28T18:07:27.725538Z",
     "shell.execute_reply": "2023-11-28T18:07:27.724211Z"
    },
    "papermill": {
     "duration": 0.063425,
     "end_time": "2023-11-28T18:07:27.728147",
     "exception": false,
     "start_time": "2023-11-28T18:07:27.664722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv('Stacking_baseline_sub.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "16a63204",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:07:27.799053Z",
     "iopub.status.busy": "2023-11-28T18:07:27.798627Z",
     "iopub.status.idle": "2023-11-28T18:07:27.810036Z",
     "shell.execute_reply": "2023-11-28T18:07:27.808988Z"
    },
    "papermill": {
     "duration": 0.04929,
     "end_time": "2023-11-28T18:07:27.812004",
     "exception": false,
     "start_time": "2023-11-28T18:07:27.762714",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Hardness</th>\n",
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      "text/plain": [
       "      id  Hardness\n",
       "0  10407  2.592411\n",
       "1  10408  2.544795\n",
       "2  10409  6.016720\n",
       "3  10410  4.313842\n",
       "4  10411  5.378780"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_1 = submission.copy()\n",
    "submission_1['Hardness'] = (stacking + voting) / 2\n",
    "submission_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a9e804d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:07:27.883341Z",
     "iopub.status.busy": "2023-11-28T18:07:27.882938Z",
     "iopub.status.idle": "2023-11-28T18:07:27.907842Z",
     "shell.execute_reply": "2023-11-28T18:07:27.906899Z"
    },
    "papermill": {
     "duration": 0.063794,
     "end_time": "2023-11-28T18:07:27.910290",
     "exception": false,
     "start_time": "2023-11-28T18:07:27.846496",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission_1.to_csv('Voting_Stacking_baseline_sub.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13866e20",
   "metadata": {
    "papermill": {
     "duration": 0.034949,
     "end_time": "2023-11-28T18:07:27.980690",
     "exception": false,
     "start_time": "2023-11-28T18:07:27.945741",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=\"6\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Baseline Modeling 2.0</h1>\n",
    "\n",
    "In this section, we build the models using `sample_weight` to optimize the `median_abosulte_error` as suggested by @ambrosm in this [post](https://www.kaggle.com/competitions/playground-series-s3e25/discussion/455888). First, we define the input and target features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a623703",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:07:28.052093Z",
     "iopub.status.busy": "2023-11-28T18:07:28.051718Z",
     "iopub.status.idle": "2023-11-28T18:07:28.086034Z",
     "shell.execute_reply": "2023-11-28T18:07:28.084739Z"
    },
    "papermill": {
     "duration": 0.073322,
     "end_time": "2023-11-28T18:07:28.088811",
     "exception": false,
     "start_time": "2023-11-28T18:07:28.015489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train.drop(columns = ['id', 'atomicweight_Average', 'Hardness'], axis = 1)\n",
    "Y = train['Hardness']\n",
    "\n",
    "test = pd.read_csv('../input/playground-series-s3e25/test.csv')\n",
    "test = test.drop(columns = ['id', 'atomicweight_Average'], axis = 1)\n",
    "\n",
    "skf = RepeatedKFold(n_splits = 10, n_repeats = 1, random_state = 42)\n",
    "kf = KFold(n_splits = 10, shuffle = True, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2976dd",
   "metadata": {
    "papermill": {
     "duration": 0.034588,
     "end_time": "2023-11-28T18:07:28.159049",
     "exception": false,
     "start_time": "2023-11-28T18:07:28.124461",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then we define the function to tune the `sample_weight`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8bfd268a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:07:28.230882Z",
     "iopub.status.busy": "2023-11-28T18:07:28.230514Z",
     "iopub.status.idle": "2023-11-28T18:07:28.239744Z",
     "shell.execute_reply": "2023-11-28T18:07:28.238373Z"
    },
    "papermill": {
     "duration": 0.048716,
     "end_time": "2023-11-28T18:07:28.242412",
     "exception": false,
     "start_time": "2023-11-28T18:07:28.193696",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def model_sample_weights(md, X_train, Y_train, X_test, Y_test, test, flag):\n",
    "\n",
    "    base_weight = 0.01\n",
    "    lower, upper = 0.06, 0.7\n",
    "\n",
    "    md_fit = md.fit(X_train, Y_train)\n",
    "    md_pred = md_fit.predict(X_test)\n",
    "    md_score = median_absolute_error(Y_test, md_pred)\n",
    "    \n",
    "    train_score = np.abs(Y_train - md_fit.predict(X_train))\n",
    "    sample_weight_tr = base_weight + ((lower < train_score) & (train_score < upper))\n",
    "\n",
    "    if flag:\n",
    "        \n",
    "        md_fit = md.fit(X_train, Y_train, model__sample_weight = sample_weight_tr)\n",
    "        md_pred = md_fit.predict(X_test)\n",
    "\n",
    "        return [cross_val_predict(md, X_train, Y_train, fit_params = {'model__sample_weight': sample_weight_tr},\n",
    "                                  cv = kf, n_jobs = -1),\n",
    "                md_pred,\n",
    "                md_fit.predict(test)]\n",
    "        \n",
    "    else: \n",
    "        \n",
    "        md_fit = md.fit(X_train, Y_train, sample_weight = sample_weight_tr)\n",
    "        md_pred = md_fit.predict(X_test)\n",
    "    \n",
    "        return [cross_val_predict(md, X_train, Y_train, fit_params = {'sample_weight': sample_weight_tr}, \n",
    "                                  cv = kf, n_jobs = -1),\n",
    "                md_pred,\n",
    "                md_fit.predict(test)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "05d06ff6",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T18:07:28.314501Z",
     "iopub.status.busy": "2023-11-28T18:07:28.314069Z",
     "iopub.status.idle": "2023-11-28T18:07:28.335598Z",
     "shell.execute_reply": "2023-11-28T18:07:28.334317Z"
    },
    "papermill": {
     "duration": 0.060159,
     "end_time": "2023-11-28T18:07:28.337702",
     "exception": false,
     "start_time": "2023-11-28T18:07:28.277543",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def hill_climbing(x, y, x_test, test):\n",
    "    \n",
    "    # Evaluating oof predictions\n",
    "    scores = {}\n",
    "    for col in x.columns:\n",
    "        scores[col] = median_absolute_error(y, x[col])\n",
    "\n",
    "    # Sorting the model scores\n",
    "    scores = {k: v for k, v in sorted(scores.items(), key = lambda item: item[1], reverse = False)}\n",
    "\n",
    "    # Sort oof_df and test_preds\n",
    "    x = x[list(scores.keys())]\n",
    "    x_test = x_test[list(scores.keys())]\n",
    "    test = test[list(scores.keys())]\n",
    "\n",
    "    STOP = False\n",
    "    current_best_ensemble = x.iloc[:,0]\n",
    "    current_best_test_preds = x_test.iloc[:,0]\n",
    "    current_best_test_test_preds = test.iloc[:,0]\n",
    "    MODELS = x.iloc[:,1:]\n",
    "    # weight_range = np.arange(-0.5, 0.51, 0.01)\n",
    "    weight_range = np.arange(0.01, 0.51, 0.01)\n",
    "    history = [median_absolute_error(y, current_best_ensemble)]\n",
    "    j = 0\n",
    "\n",
    "    while not STOP:\n",
    "        j += 1\n",
    "        potential_new_best_cv_score = median_absolute_error(y, current_best_ensemble)\n",
    "        k_best, wgt_best = None, None\n",
    "        for k in MODELS:\n",
    "            for wgt in weight_range:\n",
    "                potential_ensemble = (1 - wgt) * current_best_ensemble + wgt * MODELS[k]\n",
    "                cv_score = median_absolute_error(y, potential_ensemble)\n",
    "                if cv_score < potential_new_best_cv_score:\n",
    "                    potential_new_best_cv_score = cv_score\n",
    "                    k_best, wgt_best = k, wgt\n",
    "\n",
    "        if k_best is not None:\n",
    "            current_best_ensemble = (1 - wgt_best) * current_best_ensemble + wgt_best * MODELS[k_best]\n",
    "            current_best_test_preds = (1 - wgt_best) * current_best_test_preds + wgt_best * x_test[k_best]\n",
    "            current_best_test_test_preds = (1 - wgt_best) * current_best_test_test_preds + wgt_best * test[k_best]\n",
    "            MODELS = MODELS.drop(k_best, axis = 1)\n",
    "            if MODELS.shape[1] == 0:\n",
    "                STOP = True\n",
    "            history.append(potential_new_best_cv_score)\n",
    "        else:\n",
    "            STOP = True\n",
    "        \n",
    "    hill_ens_pred_1 = current_best_ensemble\n",
    "    hill_ens_pred_2 = current_best_test_preds\n",
    "    hill_ens_pred_3 = current_best_test_test_preds\n",
    "    \n",
    "    return [hill_ens_pred_1, hill_ens_pred_2, hill_ens_pred_3]\n",
    "\n",
    "\n",
    "md1 = Pipeline([('stand', MinMaxScaler()), \n",
    "                ('model', HistGradientBoostingRegressor(**{'loss': 'absolute_error',\n",
    "                                                            'l2_regularization': 0.027030940923710774,\n",
    "                                                            'early_stopping': False,\n",
    "                                                            'learning_rate': 0.019042088959167168,\n",
    "                                                            'max_iter': 949,\n",
    "                                                            'max_depth': 14,\n",
    "                                                            'max_bins': 255,\n",
    "                                                            'min_samples_leaf': 50,\n",
    "                                                            'max_leaf_nodes': 50}))])\n",
    "\n",
    "md2 = Pipeline([('stand', MinMaxScaler()), \n",
    "                ('model', HistGradientBoostingRegressor(**{'loss': 'absolute_error',\n",
    "                                                           'l2_regularization': 0.41075533093698835,\n",
    "                                                           'early_stopping': False,\n",
    "                                                           'learning_rate': 0.04559754489178127,\n",
    "                                                           'max_iter': 934,\n",
    "                                                           'max_depth': 14,\n",
    "                                                           'max_bins': 125,\n",
    "                                                           'min_samples_leaf': 48,\n",
    "                                                           'max_leaf_nodes': 44,\n",
    "                                                           'random_state': 10}))])\n",
    "\n",
    "md3 = LGBMRegressor(**{'objective': 'mae',\n",
    " 'n_estimators': 668,\n",
    " 'learning_rate': 0.018499866546319983,\n",
    " 'max_depth': 11,\n",
    " 'reg_alpha': 0.4618095706853164,\n",
    " 'reg_lambda': 0.07505699333277592,\n",
    " 'num_leaves': 88,\n",
    " 'subsample': 0.7994357898443023,\n",
    " 'colsample_bytree': 0.7716446660869791})\n",
    "\n",
    "md4 = LGBMRegressor(**{'objective': 'mae',\n",
    " 'n_estimators': 418,\n",
    " 'learning_rate': 0.04970452376173503,\n",
    " 'max_depth': 9,\n",
    " 'reg_alpha': 0.4671001725618798,\n",
    " 'reg_lambda': 0.046001674047501155,\n",
    " 'num_leaves': 42,\n",
    " 'subsample': 0.33242918746024247,\n",
    " 'colsample_bytree': 0.8708402901170365})\n",
    "\n",
    "md5 = LGBMRegressor(**{'objective': 'quantile',\n",
    " 'alpha': 0.5,\n",
    " 'n_estimators': 684,\n",
    " 'learning_rate': 0.016085895386454465,\n",
    " 'max_depth': 12,\n",
    " 'reg_alpha': 0.6572192610779798,\n",
    " 'reg_lambda': 0.02564469481300245,\n",
    " 'num_leaves': 55,\n",
    " 'subsample': 0.34379306525592995,\n",
    " 'colsample_bytree': 0.8093898572381174})\n",
    "\n",
    "md6 = CatBoostRegressor(**{'objective': 'MAE',\n",
    " 'iterations': 792,\n",
    " 'learning_rate': 0.033323612065351636,\n",
    " 'depth': 7,\n",
    " 'random_strength': 0.04415624028064764,\n",
    " 'bagging_temperature': 0.5522406534278442,\n",
    " 'border_count': 241,\n",
    " 'l2_leaf_reg': 8,\n",
    " 'verbose': False,\n",
    " 'task_type': 'CPU'})\n",
    "\n",
    "md7 = CatBoostRegressor(**{'objective': 'MAE',\n",
    " 'iterations': 936,\n",
    " 'learning_rate': 0.03243557362128725,\n",
    " 'depth': 11,\n",
    " 'random_strength': 0.0792068075436855,\n",
    " 'bagging_temperature': 0.08117249085611788,\n",
    " 'border_count': 212,\n",
    " 'l2_leaf_reg': 27,\n",
    " 'verbose': False,\n",
    " 'task_type': 'CPU',\n",
    " 'grow_policy': 'Lossguide'})\n",
    "\n",
    "md8 = CatBoostRegressor(**{'objective': 'MAE',\n",
    " 'iterations': 942,\n",
    " 'learning_rate': 0.02359171465972593,\n",
    " 'depth': 10,\n",
    " 'random_strength': 0.02563230761217033,\n",
    " 'bagging_temperature': 0.28083483762738604,\n",
    " 'border_count': 115,\n",
    " 'l2_leaf_reg': 20,\n",
    " 'verbose': False,\n",
    " 'task_type': 'CPU',\n",
    " 'grow_policy': 'Lossguide'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "459f0b4a",
   "metadata": {
    "papermill": {
     "duration": 0.034305,
     "end_time": "2023-11-28T18:07:28.406958",
     "exception": false,
     "start_time": "2023-11-28T18:07:28.372653",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we run the cross-validation routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d044f721",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T18:07:28.477935Z",
     "iopub.status.busy": "2023-11-28T18:07:28.477555Z",
     "iopub.status.idle": "2023-11-28T20:31:54.690871Z",
     "shell.execute_reply": "2023-11-28T20:31:54.689877Z"
    },
    "papermill": {
     "duration": 8666.251964,
     "end_time": "2023-11-28T20:31:54.693374",
     "exception": false,
     "start_time": "2023-11-28T18:07:28.441410",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Fold 0 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.4754485851972814\n",
      "Fold 0 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4551603294366666\n",
      "Fold 0 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.49637265299284294\n",
      "Fold 0 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.47573120257593304\n",
      "Fold 0 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.4841681654606802\n",
      "Fold 0 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5519512380625025\n",
      "Fold 0 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.5047116635578321\n",
      "Fold 0 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.4812663478547319\n",
      "Fold 0 ==> Median Stacker oof median absolute error score is ==> 0.47465013261147426\n",
      "Fold 0 ==> LDA Stacker oof median absolute error score is ==> 0.47719318449965886\n",
      "Fold 0 ==> Hill Climbing oof median absolute error score is ==> 0.4725418361029905\n",
      "--------------------------------------------------------------\n",
      "Fold 1 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.5018563513314938\n",
      "Fold 1 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4734566489504135\n",
      "Fold 1 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.4956719614693519\n",
      "Fold 1 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5112458740630377\n",
      "Fold 1 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.4871891243168003\n",
      "Fold 1 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5475509028168712\n",
      "Fold 1 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.49768965447201374\n",
      "Fold 1 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.5007685308041867\n",
      "Fold 1 ==> Median Stacker oof median absolute error score is ==> 0.4728377767572045\n",
      "Fold 1 ==> LDA Stacker oof median absolute error score is ==> 0.48952512271573\n",
      "Fold 1 ==> Hill Climbing oof median absolute error score is ==> 0.4859975054310346\n",
      "--------------------------------------------------------------\n",
      "Fold 2 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.48181133751827065\n",
      "Fold 2 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4885160585736763\n",
      "Fold 2 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.48742280234069035\n",
      "Fold 2 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.45267409915867685\n",
      "Fold 2 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.45471116316962057\n",
      "Fold 2 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5118289675720673\n",
      "Fold 2 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.48034408967841635\n",
      "Fold 2 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.4916059514764495\n",
      "Fold 2 ==> Median Stacker oof median absolute error score is ==> 0.4562329807714036\n",
      "Fold 2 ==> LDA Stacker oof median absolute error score is ==> 0.4704223732064672\n",
      "Fold 2 ==> Hill Climbing oof median absolute error score is ==> 0.4565399511207029\n",
      "--------------------------------------------------------------\n",
      "Fold 3 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.47373801394335446\n",
      "Fold 3 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4747683448810234\n",
      "Fold 3 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.4844473300065042\n",
      "Fold 3 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5136832253223051\n",
      "Fold 3 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.48429680037995926\n",
      "Fold 3 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5148573532485337\n",
      "Fold 3 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.4977251970232488\n",
      "Fold 3 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.4889383130318947\n",
      "Fold 3 ==> Median Stacker oof median absolute error score is ==> 0.47832036029476566\n",
      "Fold 3 ==> LDA Stacker oof median absolute error score is ==> 0.4899577344031014\n",
      "Fold 3 ==> Hill Climbing oof median absolute error score is ==> 0.47038730138089857\n",
      "--------------------------------------------------------------\n",
      "Fold 4 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.5042548181141422\n",
      "Fold 4 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5124507280323645\n",
      "Fold 4 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5071153464893774\n",
      "Fold 4 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.514788070044867\n",
      "Fold 4 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.5024582627246987\n",
      "Fold 4 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.547376858194327\n",
      "Fold 4 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.5112429190914192\n",
      "Fold 4 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.5138621342494334\n",
      "Fold 4 ==> Median Stacker oof median absolute error score is ==> 0.4997839690575696\n",
      "Fold 4 ==> LDA Stacker oof median absolute error score is ==> 0.5164998551116291\n",
      "Fold 4 ==> Hill Climbing oof median absolute error score is ==> 0.4985930477028635\n",
      "--------------------------------------------------------------\n",
      "Fold 5 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.4926077532914528\n",
      "Fold 5 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5024341484202814\n",
      "Fold 5 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5150552007195544\n",
      "Fold 5 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5075771373290676\n",
      "Fold 5 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.49269455142146157\n",
      "Fold 5 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5294819304638128\n",
      "Fold 5 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.5074274349876511\n",
      "Fold 5 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.5267903302738555\n",
      "Fold 5 ==> Median Stacker oof median absolute error score is ==> 0.49040724943410297\n",
      "Fold 5 ==> LDA Stacker oof median absolute error score is ==> 0.5152516892787917\n",
      "Fold 5 ==> Hill Climbing oof median absolute error score is ==> 0.48549431677210997\n",
      "--------------------------------------------------------------\n",
      "Fold 6 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.5570342721809327\n",
      "Fold 6 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5707619106814983\n",
      "Fold 6 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5505184604057325\n",
      "Fold 6 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5191410365677083\n",
      "Fold 6 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.5608969450155254\n",
      "Fold 6 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.6159782374930836\n",
      "Fold 6 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.5397666628023217\n",
      "Fold 6 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.5494733797186795\n",
      "Fold 6 ==> Median Stacker oof median absolute error score is ==> 0.5578892388626304\n",
      "Fold 6 ==> LDA Stacker oof median absolute error score is ==> 0.5525894332950818\n",
      "Fold 6 ==> Hill Climbing oof median absolute error score is ==> 0.5618911922295293\n",
      "--------------------------------------------------------------\n",
      "Fold 7 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.481744459905313\n",
      "Fold 7 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.49827441979607334\n",
      "Fold 7 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.4906590036242062\n",
      "Fold 7 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.4876226753139352\n",
      "Fold 7 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.5054996465865758\n",
      "Fold 7 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5230250429398775\n",
      "Fold 7 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.4974217400686922\n",
      "Fold 7 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.500280645279807\n",
      "Fold 7 ==> Median Stacker oof median absolute error score is ==> 0.4799934938688941\n",
      "Fold 7 ==> LDA Stacker oof median absolute error score is ==> 0.4782577764770939\n",
      "Fold 7 ==> Hill Climbing oof median absolute error score is ==> 0.47348568752602205\n",
      "--------------------------------------------------------------\n",
      "Fold 8 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.4878237296290484\n",
      "Fold 8 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.47560865893058235\n",
      "Fold 8 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.4721481186516412\n",
      "Fold 8 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.4705375934636158\n",
      "Fold 8 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.4622194493344469\n",
      "Fold 8 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5175504165516955\n",
      "Fold 8 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.48217516099079116\n",
      "Fold 8 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.48847585734010934\n",
      "Fold 8 ==> Median Stacker oof median absolute error score is ==> 0.4351207946910165\n",
      "Fold 8 ==> LDA Stacker oof median absolute error score is ==> 0.45825461003014567\n",
      "Fold 8 ==> Hill Climbing oof median absolute error score is ==> 0.45130766078270734\n",
      "--------------------------------------------------------------\n",
      "Fold 9 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.4829515504563051\n",
      "Fold 9 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5036519833380675\n",
      "Fold 9 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.49859166791045517\n",
      "Fold 9 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5134545108778946\n",
      "Fold 9 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.48926357270652265\n",
      "Fold 9 ==> CatBoost 1 (sample weights) oof median absolute error score is ==> 0.5272735432564104\n",
      "Fold 9 ==> CatBoost 2 (sample weights) oof median absolute error score is ==> 0.4910043904655568\n",
      "Fold 9 ==> CatBoost 3 (sample weights) oof median absolute error score is ==> 0.49521313224250996\n",
      "Fold 9 ==> Median Stacker oof median absolute error score is ==> 0.478237030225817\n",
      "Fold 9 ==> LDA Stacker oof median absolute error score is ==> 0.47900306105569834\n",
      "Fold 9 ==> Hill Climbing oof median absolute error score is ==> 0.48342386333079645\n"
     ]
    }
   ],
   "source": [
    "median_scores, median_preds, median_preds_test = list(), list(), list()\n",
    "LDA_scores, LDA_preds, LDA_preds_test = list(), list(), list()\n",
    "hill_scores, hill_preds, hill_preds_test = list(), list(), list()\n",
    "\n",
    "for i, (train_idx, test_idx) in enumerate(skf.split(X, Y)):\n",
    "\n",
    "    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
    "    Y_train, Y_test = Y.iloc[train_idx], Y.iloc[test_idx]\n",
    "\n",
    "    print('--------------------------------------------------------------')\n",
    "\n",
    "    md_train_preds = pd.DataFrame()\n",
    "    md_test_preds = pd.DataFrame()\n",
    "    md_test_test_preds = pd.DataFrame()\n",
    "\n",
    "    ###########\n",
    "    ## HGB 1 ##\n",
    "    ###########\n",
    "    \n",
    "    HGB_md_1 = model_sample_weights(md1, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['HGB_1'] = HGB_md_1[0]\n",
    "    md_test_preds['HGB_1'] = HGB_md_1[1]\n",
    "    md_test_test_preds['HGB_1'] = HGB_md_1[2]\n",
    "\n",
    "    HGB_score_1 = median_absolute_error(Y_test, HGB_md_1[1])\n",
    "    print('Fold', i, '==> HGB 1 (sample weights) oof median absolute error score is ==>', HGB_score_1)\n",
    "    \n",
    "    ###########\n",
    "    ## HGB 2 ##\n",
    "    ###########\n",
    "\n",
    "    HGB_md_2 = model_sample_weights(md2, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['HGB_2'] = HGB_md_2[0]\n",
    "    md_test_preds['HGB_2'] = HGB_md_2[1]\n",
    "    md_test_test_preds['HGB_2'] = HGB_md_2[2]\n",
    "\n",
    "    HGB_score_2 = median_absolute_error(Y_test, HGB_md_2[1])\n",
    "    print('Fold', i, '==> HGB 2 (sample weights) oof median absolute error score is ==>', HGB_score_2)\n",
    "    \n",
    "    ############\n",
    "    ## LGBM 1 ##\n",
    "    ############\n",
    "\n",
    "    LGBM_md_1 = model_sample_weights(md3, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['LGBM_1'] = LGBM_md_1[0]\n",
    "    md_test_preds['LGBM_1'] = LGBM_md_1[1]\n",
    "    md_test_test_preds['LGBM_1'] = LGBM_md_1[2]\n",
    "\n",
    "    LGBM_score_1 = median_absolute_error(Y_test, LGBM_md_1[1])\n",
    "    print('Fold', i, '==> LGBM 1 (sample weights) oof median absolute error score is ==>', LGBM_score_1)\n",
    "    \n",
    "    ############\n",
    "    ## LGBM 2 ##\n",
    "    ############\n",
    "\n",
    "    LGBM_md_2 = model_sample_weights(md4, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['LGBM_2'] = LGBM_md_2[0]\n",
    "    md_test_preds['LGBM_2'] = LGBM_md_2[1]\n",
    "    md_test_test_preds['LGBM_2'] = LGBM_md_2[2]\n",
    "\n",
    "    LGBM_score_2 = median_absolute_error(Y_test, LGBM_md_2[1])\n",
    "    print('Fold', i, '==> LGBM 2 (sample weights) oof median absolute error score is ==>', LGBM_score_2)\n",
    "    \n",
    "    ############\n",
    "    ## LGBM 3 ##\n",
    "    ############\n",
    "\n",
    "    LGBM_md_3 = model_sample_weights(md5, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['LGBM_3'] = LGBM_md_3[0]\n",
    "    md_test_preds['LGBM_3'] = LGBM_md_3[1]\n",
    "    md_test_test_preds['LGBM_3'] = LGBM_md_3[2]\n",
    "\n",
    "    LGBM_score_3 = median_absolute_error(Y_test, LGBM_md_3[1])\n",
    "    print('Fold', i, '==> LGBM 3 (sample weights) oof median absolute error score is ==>', LGBM_score_3)\n",
    "    \n",
    "    ################\n",
    "    ## CatBoost 1 ##\n",
    "    ################\n",
    "\n",
    "    Cat_md_1 = model_sample_weights(md6, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['Cat_1'] = Cat_md_1[0]\n",
    "    md_test_preds['Cat_1'] = Cat_md_1[1]\n",
    "    md_test_test_preds['Cat_1'] = Cat_md_1[2]\n",
    "\n",
    "    Cat_score_1 = median_absolute_error(Y_test, Cat_md_1[1])\n",
    "    print('Fold', i, '==> CatBoost 1 (sample weights) oof median absolute error score is ==>', Cat_score_1)\n",
    "    \n",
    "    ################\n",
    "    ## CatBoost 2 ##\n",
    "    ################\n",
    "\n",
    "    Cat_md_2 = model_sample_weights(md7, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['Cat_2'] = Cat_md_2[0]\n",
    "    md_test_preds['Cat_2'] = Cat_md_2[1]\n",
    "    md_test_test_preds['Cat_2'] = Cat_md_2[2]\n",
    "\n",
    "    Cat_score_2 = median_absolute_error(Y_test, Cat_md_2[1])\n",
    "    print('Fold', i, '==> CatBoost 2 (sample weights) oof median absolute error score is ==>', Cat_score_2)\n",
    "    \n",
    "    ################\n",
    "    ## CatBoost 3 ##\n",
    "    ################\n",
    "\n",
    "    Cat_md_3 = model_sample_weights(md8, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['Cat_3'] = Cat_md_3[0]\n",
    "    md_test_preds['Cat_3'] = Cat_md_3[1]\n",
    "    md_test_test_preds['Cat_3'] = Cat_md_3[2]\n",
    "\n",
    "    Cat_score_3 = median_absolute_error(Y_test, Cat_md_3[1])\n",
    "    print('Fold', i, '==> CatBoost 3 (sample weights) oof median absolute error score is ==>', Cat_score_3)\n",
    "    \n",
    "    ####################\n",
    "    ## Median Stacker ##\n",
    "    ####################\n",
    "\n",
    "    median_pred = md_test_preds.apply(np.median, axis = 1)\n",
    "    median_score = median_absolute_error(Y_test, median_pred)\n",
    "    median_scores.append(median_score)\n",
    "    median_preds.append(median_pred)\n",
    "\n",
    "    median_pred_test = md_test_test_preds.apply(np.median, axis = 1)\n",
    "    median_preds_test.append(median_pred_test)\n",
    "\n",
    "    print('Fold', i, '==> Median Stacker oof median absolute error score is ==>', median_score)\n",
    "    \n",
    "    #################\n",
    "    ## LDA Stacker ##\n",
    "    #################\n",
    "\n",
    "    LDA_md = LADRegression().fit(md_train_preds.iloc[:, 0:5], Y_train)\n",
    "    LDA_pred = LDA_md.predict(md_test_preds.iloc[:, 0:5])\n",
    "    LDA_pred_test = LDA_md.predict(md_test_test_preds.iloc[:, 0:5])\n",
    "    LDA_score = median_absolute_error(Y_test, LDA_pred)\n",
    "    LDA_scores.append(LDA_score)\n",
    "    LDA_preds.append(LDA_pred)\n",
    "    LDA_preds_test.append(LDA_pred_test)\n",
    "    \n",
    "    print('Fold', i, '==> LDA Stacker oof median absolute error score is ==>', LDA_score)\n",
    "    \n",
    "    ###################\n",
    "    ## Hill Climbing ##\n",
    "    ###################\n",
    "\n",
    "    hill_results = hill_climbing(md_train_preds, Y_train, md_test_preds, md_test_test_preds)\n",
    "    hill_train = hill_results[0]\n",
    "    train_score = median_absolute_error(Y_train, hill_train)\n",
    "    \n",
    "    hill_test = hill_results[1]\n",
    "    test_score = median_absolute_error(Y_test, hill_test)\n",
    "    hill_scores.append(test_score)\n",
    "    hill_preds.append(hill_test)\n",
    "    \n",
    "    hill_preds_test.append(hill_results[2])\n",
    "    \n",
    "    # # print('Fold', i, '==> Hill Climbing in-sample median absolute error score is ==>', train_score)\n",
    "    print('Fold', i, '==> Hill Climbing oof median absolute error score is ==>', test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "40d40b18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T20:31:54.779757Z",
     "iopub.status.busy": "2023-11-28T20:31:54.779114Z",
     "iopub.status.idle": "2023-11-28T20:31:54.785377Z",
     "shell.execute_reply": "2023-11-28T20:31:54.784377Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The oof median absolute error score of the Median Stacker is 0.48234730265748793\n",
      "The oof median absolute error score of the LDA Stacker is 0.49269548400733976\n",
      "The oof median absolute error score of the Hill Climbing is 0.4839662362379655\n"
     ]
    }
   ],
   "source": [
    "print(f\"The oof median absolute error score of the Median Stacker is {np.mean(median_scores)}\")\n",
    "print(f\"The oof median absolute error score of the LDA Stacker is {np.mean(LDA_scores)}\")\n",
    "print(f\"The oof median absolute error score of the Hill Climbing is {np.mean(hill_scores)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8d3728c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T20:31:54.872598Z",
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    },
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     "duration": 2.10324,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10407</td>\n",
       "      <td>2.386626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10408</td>\n",
       "      <td>2.520987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10409</td>\n",
       "      <td>5.790057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10410</td>\n",
       "      <td>4.440443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10411</td>\n",
       "      <td>4.925160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  Hardness\n",
       "0  10407  2.386626\n",
       "1  10408  2.520987\n",
       "2  10409  5.790057\n",
       "3  10410  4.440443\n",
       "4  10411  4.925160"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_pred_cv = pd.DataFrame(median_preds_test).apply(np.median, axis = 0)\n",
    "LDA_pred_cv = pd.DataFrame(LDA_preds_test).apply(np.median, axis = 0)\n",
    "hill_pred_cv = pd.DataFrame(hill_preds_test).apply(np.median, axis = 0)\n",
    "\n",
    "model_preds = pd.DataFrame()\n",
    "model_preds['LDA'] = LDA_pred_cv\n",
    "model_preds['MED'] = med_pred_cv\n",
    "model_preds['HILL'] = hill_pred_cv\n",
    "\n",
    "submission_2 = submission.copy()\n",
    "submission_2['Hardness'] = model_preds.apply(np.median, axis = 1)\n",
    "submission_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "564b5713",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T20:31:57.017532Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission_2.to_csv('Median_LDA_Hill_sample_weight_sub.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d96043",
   "metadata": {
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     "duration": 0.04162,
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   },
   "source": [
    "<a id=\"7\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Baseline Modeling 3.0</h1>\n",
    "\n",
    "In the previuous two baseline modeling, `atomicweight_Average` was removed because it is highly correlated with `allelectrons_Average`. In this version, we keep it in the models. First, we define the `input` and `target`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a4b6933e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T20:31:57.212544Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train.drop(columns = ['id', 'Hardness'], axis = 1)\n",
    "Y = train['Hardness']\n",
    "\n",
    "test = pd.read_csv('../input/playground-series-s3e25/test.csv')\n",
    "test = test.drop(columns = ['id'], axis = 1)\n",
    "\n",
    "skf = RepeatedKFold(n_splits = 10, n_repeats = 1, random_state = 42)\n",
    "kf = KFold(n_splits = 10, shuffle = True, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "565e1b0e",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2023-11-28T20:31:57.331335Z",
     "iopub.status.busy": "2023-11-28T20:31:57.330671Z",
     "iopub.status.idle": "2023-11-28T20:31:57.357690Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "md1 = HistGradientBoostingRegressor(**{'loss': 'absolute_error', \n",
    "                                       'l2_regularization': 1.3964176081789386, \n",
    "                                       'early_stopping': False, \n",
    "                                       'learning_rate': 0.016009625548136137, \n",
    "                                       'max_iter': 1533, \n",
    "                                       'max_depth': 10, \n",
    "                                       'max_bins': 118, \n",
    "                                       'min_samples_leaf': 46, \n",
    "                                       'max_leaf_nodes': 41, \n",
    "                                       'random_state': 10})\n",
    "\n",
    "\n",
    "md2 = Pipeline([('stand', MinMaxScaler()), \n",
    "                ('model', HistGradientBoostingRegressor(**{'loss': 'absolute_error', \n",
    "                                                           'l2_regularization': 0.0773440762323129, \n",
    "                                                           'early_stopping': False, \n",
    "                                                           'learning_rate': 0.07665261989200847, \n",
    "                                                           'max_iter': 244, 'max_depth': 8, \n",
    "                                                           'max_bins': 67, \n",
    "                                                           'min_samples_leaf': 26, \n",
    "                                                           'max_leaf_nodes': 45, \n",
    "                                                           'random_state': 10}))])\n",
    "\n",
    "md3 = Pipeline([('stand', StandardScaler()), \n",
    "                ('model', HistGradientBoostingRegressor(**{'loss': 'absolute_error', \n",
    "                                                           'l2_regularization': 0.03234463289342263, \n",
    "                                                           'early_stopping': False, \n",
    "                                                           'learning_rate': 0.013152992006060682, \n",
    "                                                           'max_iter': 1791, \n",
    "                                                           'max_depth': 7, \n",
    "                                                           'max_bins': 97, \n",
    "                                                           'min_samples_leaf': 16, \n",
    "                                                           'max_leaf_nodes': 33, \n",
    "                                                           'random_state': 10}))])\n",
    "\n",
    "md4 = Pipeline([('stand', RobustScaler()), \n",
    "                ('model', HistGradientBoostingRegressor(**{'loss': 'absolute_error', \n",
    "                                                           'l2_regularization': 0.16660827795253544, \n",
    "                                                           'early_stopping': False, \n",
    "                                                           'learning_rate': 0.0484806700732674, \n",
    "                                                           'max_iter': 657, \n",
    "                                                           'max_depth': 13, \n",
    "                                                           'max_bins': 172, \n",
    "                                                           'min_samples_leaf': 44, \n",
    "                                                           'max_leaf_nodes': 48, \n",
    "                                                           'random_state': 10}))])\n",
    "\n",
    "md5 = LGBMRegressor(**{'objective': 'mae', \n",
    "                       'n_estimators': 1816, \n",
    "                       'learning_rate': 0.011960149701703357, \n",
    "                       'max_depth': 11, \n",
    "                       'reg_alpha': 2.943938939425285, \n",
    "                       'reg_lambda': 0.06527831358565354, \n",
    "                       'num_leaves': 98, \n",
    "                       'subsample': 0.7831190787928901, \n",
    "                       'colsample_bytree': 0.7169192797229946})\n",
    "\n",
    "\n",
    "md6 = Pipeline([('stand', MinMaxScaler()), \n",
    "                ('model', LGBMRegressor(**{'objective': 'mae', \n",
    "                                           'n_estimators': 1461, \n",
    "                                           'learning_rate': 0.013343645844387813, \n",
    "                                           'max_depth': 8, \n",
    "                                           'reg_alpha': 0.5296906765102644, \n",
    "                                           'reg_lambda': 0.4622644322255429, \n",
    "                                           'num_leaves': 55, \n",
    "                                           'subsample': 0.44912198107158186, \n",
    "                                           'colsample_bytree': 0.6084534642705367}))])\n",
    "\n",
    "md7 = Pipeline([('stand', StandardScaler()), \n",
    "                ('model', LGBMRegressor(**{'objective': 'mae', \n",
    "                                           'n_estimators': 548, \n",
    "                                           'learning_rate': 0.024315021778078507, \n",
    "                                           'max_depth': 11, \n",
    "                                           'reg_alpha': 1.9374435679081374, \n",
    "                                           'reg_lambda': 2.942038621536813, \n",
    "                                           'num_leaves': 61, \n",
    "                                           'subsample': 0.49955373075773585, \n",
    "                                           'colsample_bytree': 0.5690886596910012}))])\n",
    "\n",
    "\n",
    "md8 = Pipeline([('stand', RobustScaler()), \n",
    "                ('model', LGBMRegressor(**{'objective': 'mae', \n",
    "                                           'n_estimators': 267, \n",
    "                                           'learning_rate': 0.05355930668936292, \n",
    "                                           'max_depth': 9, \n",
    "                                           'reg_alpha': 0.14455154126384284, \n",
    "                                           'reg_lambda': 0.04075237417599898, \n",
    "                                           'num_leaves': 62, \n",
    "                                           'subsample': 0.7416222141008473, \n",
    "                                           'colsample_bytree': 0.8919698784847256}))])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98770ac0",
   "metadata": {
    "papermill": {
     "duration": 0.041398,
     "end_time": "2023-11-28T20:31:57.443394",
     "exception": false,
     "start_time": "2023-11-28T20:31:57.401996",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we run the usual cross-validation routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ae83c4a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-28T20:31:57.529338Z",
     "iopub.status.busy": "2023-11-28T20:31:57.528702Z",
     "iopub.status.idle": "2023-11-29T00:11:44.796518Z",
     "shell.execute_reply": "2023-11-29T00:11:44.795284Z"
    },
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     "duration": 13187.314102,
     "end_time": "2023-11-29T00:11:44.799358",
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     "start_time": "2023-11-28T20:31:57.485256",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Fold 0 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.48379327874421296\n",
      "Fold 0 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5016982316256149\n",
      "Fold 0 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.47190091829722824\n",
      "Fold 0 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.5052002773154873\n",
      "Fold 0 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5022465067271114\n",
      "Fold 0 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.48217229699334574\n",
      "Fold 0 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.4827805814398749\n",
      "Fold 0 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.4806815508243174\n",
      "Fold 0 ==> Median Stacker oof median absolute error score is ==> 0.47645228609469825\n",
      "Fold 0 ==> LDA Stacker oof median absolute error score is ==> 0.4941972895048128\n",
      "Fold 0 ==> Hill Climbing oof median absolute error score is ==> 0.4715824750166018\n",
      "--------------------------------------------------------------\n",
      "Fold 1 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.48529342515338847\n",
      "Fold 1 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5200911395383265\n",
      "Fold 1 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.49273527148106444\n",
      "Fold 1 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.5045724122176205\n",
      "Fold 1 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5187548353480191\n",
      "Fold 1 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5104920825362349\n",
      "Fold 1 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.5034500869515695\n",
      "Fold 1 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.5220817272214493\n",
      "Fold 1 ==> Median Stacker oof median absolute error score is ==> 0.4859987608234766\n",
      "Fold 1 ==> LDA Stacker oof median absolute error score is ==> 0.49340564365063067\n",
      "Fold 1 ==> Hill Climbing oof median absolute error score is ==> 0.506042397286627\n",
      "--------------------------------------------------------------\n",
      "Fold 2 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.47881109125975296\n",
      "Fold 2 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4752688013814188\n",
      "Fold 2 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.45711365144169935\n",
      "Fold 2 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.49732300088401393\n",
      "Fold 2 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.4996500225924798\n",
      "Fold 2 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.459284139245522\n",
      "Fold 2 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.4567597640166374\n",
      "Fold 2 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.4535920489643983\n",
      "Fold 2 ==> Median Stacker oof median absolute error score is ==> 0.45527490643884416\n",
      "Fold 2 ==> LDA Stacker oof median absolute error score is ==> 0.45524748608420573\n",
      "Fold 2 ==> Hill Climbing oof median absolute error score is ==> 0.45474967842821634\n",
      "--------------------------------------------------------------\n",
      "Fold 3 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.47774528411257977\n",
      "Fold 3 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4791392289401495\n",
      "Fold 3 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.4654821019238091\n",
      "Fold 3 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.4936084675650445\n",
      "Fold 3 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.48522833675540067\n",
      "Fold 3 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.4653099219528185\n",
      "Fold 3 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.46966795737679856\n",
      "Fold 3 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.4760489182490266\n",
      "Fold 3 ==> Median Stacker oof median absolute error score is ==> 0.4589582272687025\n",
      "Fold 3 ==> LDA Stacker oof median absolute error score is ==> 0.4556142634955562\n",
      "Fold 3 ==> Hill Climbing oof median absolute error score is ==> 0.4683442042186483\n",
      "--------------------------------------------------------------\n",
      "Fold 4 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.4982884245923078\n",
      "Fold 4 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5189206047889252\n",
      "Fold 4 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.514575108144383\n",
      "Fold 4 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.5393736777074682\n",
      "Fold 4 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5226729486315795\n",
      "Fold 4 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5198457388077662\n",
      "Fold 4 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.5009389892602156\n",
      "Fold 4 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.50509857268632\n",
      "Fold 4 ==> Median Stacker oof median absolute error score is ==> 0.4961968064902802\n",
      "Fold 4 ==> LDA Stacker oof median absolute error score is ==> 0.5022177520701359\n",
      "Fold 4 ==> Hill Climbing oof median absolute error score is ==> 0.511106848886743\n",
      "--------------------------------------------------------------\n",
      "Fold 5 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.49240208343369307\n",
      "Fold 5 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5043797422444767\n",
      "Fold 5 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.49633625203434484\n",
      "Fold 5 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.5153893113662784\n",
      "Fold 5 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5259018862992146\n",
      "Fold 5 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.49570549553781085\n",
      "Fold 5 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.49004126856842767\n",
      "Fold 5 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.5034855401131688\n",
      "Fold 5 ==> Median Stacker oof median absolute error score is ==> 0.48000208476735473\n",
      "Fold 5 ==> LDA Stacker oof median absolute error score is ==> 0.48333337315072167\n",
      "Fold 5 ==> Hill Climbing oof median absolute error score is ==> 0.4781335432212126\n",
      "--------------------------------------------------------------\n",
      "Fold 6 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.5362279462507633\n",
      "Fold 6 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5221918178719944\n",
      "Fold 6 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.5562006832742594\n",
      "Fold 6 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.5337675193884834\n",
      "Fold 6 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5542961840072382\n",
      "Fold 6 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.5232955793154339\n",
      "Fold 6 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.5258808326728106\n",
      "Fold 6 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.5417515693947061\n",
      "Fold 6 ==> Median Stacker oof median absolute error score is ==> 0.4994069379786703\n",
      "Fold 6 ==> LDA Stacker oof median absolute error score is ==> 0.5253091529167615\n",
      "Fold 6 ==> Hill Climbing oof median absolute error score is ==> 0.5202763148859102\n",
      "--------------------------------------------------------------\n",
      "Fold 7 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.5033238061289698\n",
      "Fold 7 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4850539485911738\n",
      "Fold 7 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.4890583167933721\n",
      "Fold 7 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.5008024776711011\n",
      "Fold 7 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.4950073830760955\n",
      "Fold 7 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.48243693396902243\n",
      "Fold 7 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.49937990962784706\n",
      "Fold 7 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.49103031853194246\n",
      "Fold 7 ==> Median Stacker oof median absolute error score is ==> 0.4825041578577207\n",
      "Fold 7 ==> LDA Stacker oof median absolute error score is ==> 0.4993310956452728\n",
      "Fold 7 ==> Hill Climbing oof median absolute error score is ==> 0.48024603317004666\n",
      "--------------------------------------------------------------\n",
      "Fold 8 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.46746346237853365\n",
      "Fold 8 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.4802143843395419\n",
      "Fold 8 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.4676216391638053\n",
      "Fold 8 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.49534995023197226\n",
      "Fold 8 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5042334628734109\n",
      "Fold 8 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.47726501924376574\n",
      "Fold 8 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.46837323372942574\n",
      "Fold 8 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.4709385328337221\n",
      "Fold 8 ==> Median Stacker oof median absolute error score is ==> 0.4548452436162358\n",
      "Fold 8 ==> LDA Stacker oof median absolute error score is ==> 0.4720370748978344\n",
      "Fold 8 ==> Hill Climbing oof median absolute error score is ==> 0.45294967212781545\n",
      "--------------------------------------------------------------\n",
      "Fold 9 ==> HGB 1 (sample weights) oof median absolute error score is ==> 0.49208327827105114\n",
      "Fold 9 ==> HGB 2 (sample weights) oof median absolute error score is ==> 0.5060810064494798\n",
      "Fold 9 ==> HGB 3 (sample weights) oof median absolute error score is ==> 0.4965244677131251\n",
      "Fold 9 ==> HGB 4 (sample weights) oof median absolute error score is ==> 0.4831709615748885\n",
      "Fold 9 ==> LGBM 1 (sample weights) oof median absolute error score is ==> 0.5305999958931924\n",
      "Fold 9 ==> LGBM 2 (sample weights) oof median absolute error score is ==> 0.4995976610837589\n",
      "Fold 9 ==> LGBM 3 (sample weights) oof median absolute error score is ==> 0.48540518373240715\n",
      "Fold 9 ==> LGBM 4 (sample weights) oof median absolute error score is ==> 0.5033591479205883\n",
      "Fold 9 ==> Median Stacker oof median absolute error score is ==> 0.47624587229322035\n",
      "Fold 9 ==> LDA Stacker oof median absolute error score is ==> 0.4794675104935311\n",
      "Fold 9 ==> Hill Climbing oof median absolute error score is ==> 0.4900248472516706\n"
     ]
    }
   ],
   "source": [
    "md1_preds, md2_preds, md3_preds, md4_preds = list(), list(), list(), list()\n",
    "md5_preds, md6_preds, md7_preds, md8_preds = list(), list(), list(), list()\n",
    "\n",
    "vot_scores, vot_preds, vot_preds_test = list(), list(), list()\n",
    "median_scores, median_preds, median_preds_test = list(), list(), list()\n",
    "LDA_scores, LDA_preds, LDA_preds_test = list(), list(), list()\n",
    "hill_scores, hill_preds, hill_preds_test = list(), list(), list()\n",
    "\n",
    "for i, (train_idx, test_idx) in enumerate(skf.split(X, Y)):\n",
    "\n",
    "    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
    "    Y_train, Y_test = Y.iloc[train_idx], Y.iloc[test_idx]\n",
    "\n",
    "    print('--------------------------------------------------------------')\n",
    "\n",
    "    md_train_preds = pd.DataFrame()\n",
    "    md_test_preds = pd.DataFrame()\n",
    "    md_test_test_preds = pd.DataFrame()\n",
    "\n",
    "    ###########\n",
    "    ## HGB 1 ##\n",
    "    ###########\n",
    "    \n",
    "    HGB_md_1 = model_sample_weights(md1, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['HGB_1'] = HGB_md_1[0]\n",
    "    md_test_preds['HGB_1'] = HGB_md_1[1]\n",
    "    md_test_test_preds['HGB_1'] = HGB_md_1[2]\n",
    "\n",
    "    HGB_score_1 = median_absolute_error(Y_test, HGB_md_1[1])\n",
    "    print('Fold', i, '==> HGB 1 (sample weights) oof median absolute error score is ==>', HGB_score_1)\n",
    "    # md1_preds.append(HGB_md_1[1])\n",
    "    \n",
    "    ###########\n",
    "    ## HGB 2 ##\n",
    "    ###########\n",
    "\n",
    "    HGB_md_2 = model_sample_weights(md2, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['HGB_2'] = HGB_md_2[0]\n",
    "    md_test_preds['HGB_2'] = HGB_md_2[1]\n",
    "    md_test_test_preds['HGB_2'] = HGB_md_2[2]\n",
    "\n",
    "    HGB_score_2 = median_absolute_error(Y_test, HGB_md_2[1])\n",
    "    print('Fold', i, '==> HGB 2 (sample weights) oof median absolute error score is ==>', HGB_score_2)\n",
    "    # md2_preds.append(HGB_md_2[1])\n",
    "\n",
    "    ###########\n",
    "    ## HGB 3 ##\n",
    "    ###########\n",
    "\n",
    "    HGB_md_3 = model_sample_weights(md3, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['HGB_3'] = HGB_md_3[0]\n",
    "    md_test_preds['HGB_3'] = HGB_md_3[1]\n",
    "    md_test_test_preds['HGB_3'] = HGB_md_3[2]\n",
    "\n",
    "    HGB_score_3 = median_absolute_error(Y_test, HGB_md_3[1])\n",
    "    print('Fold', i, '==> HGB 3 (sample weights) oof median absolute error score is ==>', HGB_score_3)\n",
    "    # md2_preds.append(HGB_md_3[1])\n",
    "\n",
    "    ###########\n",
    "    ## HGB 4 ##\n",
    "    ###########\n",
    "\n",
    "    HGB_md_4 = model_sample_weights(md4, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['HGB_4'] = HGB_md_4[0]\n",
    "    md_test_preds['HGB_4'] = HGB_md_4[1]\n",
    "    md_test_test_preds['HGB_4'] = HGB_md_4[2]\n",
    "\n",
    "    HGB_score_4 = median_absolute_error(Y_test, HGB_md_4[1])\n",
    "    print('Fold', i, '==> HGB 4 (sample weights) oof median absolute error score is ==>', HGB_score_4)\n",
    "    # md2_preds.append(HGB_md_3[1])\n",
    "\n",
    "    ############\n",
    "    ## LGBM 1 ##\n",
    "    ############\n",
    "\n",
    "    LGBM_md_1 = model_sample_weights(md5, X_train, Y_train, X_test, Y_test, test, False)\n",
    "\n",
    "    md_train_preds['LGBM_1'] = LGBM_md_1[0]\n",
    "    md_test_preds['LGBM_1'] = LGBM_md_1[1]\n",
    "    md_test_test_preds['LGBM_1'] = LGBM_md_1[2]\n",
    "\n",
    "    LGBM_score_1 = median_absolute_error(Y_test, LGBM_md_1[1])\n",
    "    print('Fold', i, '==> LGBM 1 (sample weights) oof median absolute error score is ==>', LGBM_score_1)\n",
    "    md3_preds.append(LGBM_md_1[1])\n",
    "    \n",
    "    ############\n",
    "    ## LGBM 2 ##\n",
    "    ############\n",
    "\n",
    "    LGBM_md_2 = model_sample_weights(md6, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['LGBM_2'] = LGBM_md_2[0]\n",
    "    md_test_preds['LGBM_2'] = LGBM_md_2[1]\n",
    "    md_test_test_preds['LGBM_2'] = LGBM_md_2[2]\n",
    "\n",
    "    LGBM_score_2 = median_absolute_error(Y_test, LGBM_md_2[1])\n",
    "    print('Fold', i, '==> LGBM 2 (sample weights) oof median absolute error score is ==>', LGBM_score_2)\n",
    "    md4_preds.append(LGBM_md_2[1])\n",
    "    \n",
    "    ############\n",
    "    ## LGBM 3 ##\n",
    "    ############\n",
    "\n",
    "    LGBM_md_3 = model_sample_weights(md7, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['LGBM_3'] = LGBM_md_3[0]\n",
    "    md_test_preds['LGBM_3'] = LGBM_md_3[1]\n",
    "    md_test_test_preds['LGBM_3'] = LGBM_md_3[2]\n",
    "\n",
    "    LGBM_score_3 = median_absolute_error(Y_test, LGBM_md_3[1])\n",
    "    print('Fold', i, '==> LGBM 3 (sample weights) oof median absolute error score is ==>', LGBM_score_3)\n",
    "    # md5_preds.append(LGBM_md_3[1])\n",
    "\n",
    "    ############\n",
    "    ## LGBM 4 ##\n",
    "    ############\n",
    "\n",
    "    LGBM_md_4 = model_sample_weights(md8, X_train, Y_train, X_test, Y_test, test, True)\n",
    "\n",
    "    md_train_preds['LGBM_4'] = LGBM_md_4[0]\n",
    "    md_test_preds['LGBM_4'] = LGBM_md_4[1]\n",
    "    md_test_test_preds['LGBM_4'] = LGBM_md_4[2]\n",
    "\n",
    "    LGBM_score_4 = median_absolute_error(Y_test, LGBM_md_4[1])\n",
    "    print('Fold', i, '==> LGBM 4 (sample weights) oof median absolute error score is ==>', LGBM_score_4)\n",
    "    # md5_preds.append(LGBM_md_3[1])\n",
    "    \n",
    "    #######################\n",
    "    ## Median Prediction ##\n",
    "    #######################\n",
    "\n",
    "    median_pred = md_test_preds.apply(np.median, axis = 1)\n",
    "    median_score = median_absolute_error(Y_test, median_pred)\n",
    "    median_scores.append(median_score)\n",
    "    median_preds.append(median_pred)\n",
    "\n",
    "    median_pred_test = md_test_test_preds.apply(np.median, axis = 1)\n",
    "    median_preds_test.append(median_pred_test)\n",
    "\n",
    "    print('Fold', i, '==> Median Stacker oof median absolute error score is ==>', median_score)\n",
    "    \n",
    "    ########################\n",
    "    ## Stacking Regressor ##\n",
    "    ########################\n",
    "\n",
    "    LDA_md = LADRegression().fit(md_train_preds, Y_train)\n",
    "    LDA_pred = LDA_md.predict(md_test_preds)\n",
    "    LDA_pred_test = LDA_md.predict(md_test_test_preds)\n",
    "    LDA_score = median_absolute_error(Y_test, LDA_pred)\n",
    "    LDA_scores.append(LDA_score)\n",
    "    LDA_preds.append(LDA_pred)\n",
    "    LDA_preds_test.append(LDA_pred_test)\n",
    "    \n",
    "    print('Fold', i, '==> LDA Stacker oof median absolute error score is ==>', LDA_score)\n",
    "    \n",
    "    ###################\n",
    "    ## Hill Climbing ##\n",
    "    ###################\n",
    "\n",
    "    hill_results = hill_climbing(md_train_preds, Y_train, md_test_preds, md_test_test_preds)\n",
    "    hill_train = hill_results[0]\n",
    "    train_score = median_absolute_error(Y_train, hill_train)\n",
    "    \n",
    "    hill_test = hill_results[1]\n",
    "    test_score = median_absolute_error(Y_test, hill_test)\n",
    "    hill_scores.append(test_score)\n",
    "    hill_preds.append(hill_test)\n",
    "    \n",
    "    hill_preds_test.append(hill_results[2])\n",
    "    \n",
    "    # # print('Fold', i, '==> Hill Climbing in-sample median absolute error score is ==>', train_score)\n",
    "    print('Fold', i, '==> Hill Climbing oof median absolute error score is ==>', test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "78d772d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:11:44.904878Z",
     "iopub.status.busy": "2023-11-29T00:11:44.904459Z",
     "iopub.status.idle": "2023-11-29T00:11:44.910360Z",
     "shell.execute_reply": "2023-11-29T00:11:44.909332Z"
    },
    "papermill": {
     "duration": 0.063085,
     "end_time": "2023-11-29T00:11:44.912588",
     "exception": false,
     "start_time": "2023-11-29T00:11:44.849503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The oof median absolute error score of the Median Stacker is 0.47658852836292037\n",
      "The oof median absolute error score of the LDA Stacker is 0.4860160641909463\n",
      "The oof median absolute error score of the Hill Climbing is 0.4833456014493492\n"
     ]
    }
   ],
   "source": [
    "print(f\"The oof median absolute error score of the Median Stacker is {np.mean(median_scores)}\")\n",
    "print(f\"The oof median absolute error score of the LDA Stacker is {np.mean(LDA_scores)}\")\n",
    "print(f\"The oof median absolute error score of the Hill Climbing is {np.mean(hill_scores)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "18dd5d3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:11:45.012981Z",
     "iopub.status.busy": "2023-11-29T00:11:45.012552Z",
     "iopub.status.idle": "2023-11-29T00:11:47.082156Z",
     "shell.execute_reply": "2023-11-29T00:11:47.080716Z"
    },
    "papermill": {
     "duration": 2.123789,
     "end_time": "2023-11-29T00:11:47.085238",
     "exception": false,
     "start_time": "2023-11-29T00:11:44.961449",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10407</td>\n",
       "      <td>2.402132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10408</td>\n",
       "      <td>2.479047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10409</td>\n",
       "      <td>5.818545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10410</td>\n",
       "      <td>4.756925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10411</td>\n",
       "      <td>4.954281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  Hardness\n",
       "0  10407  2.402132\n",
       "1  10408  2.479047\n",
       "2  10409  5.818545\n",
       "3  10410  4.756925\n",
       "4  10411  4.954281"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "med_pred_cv = pd.DataFrame(median_preds_test).apply(np.median, axis = 0)\n",
    "LDA_pred_cv = pd.DataFrame(LDA_preds_test).apply(np.median, axis = 0)\n",
    "hill_pred_cv = pd.DataFrame(hill_preds_test).apply(np.median, axis = 0)\n",
    "\n",
    "model_preds = pd.DataFrame()\n",
    "model_preds['LDA'] = LDA_pred_cv\n",
    "model_preds['MED'] = med_pred_cv\n",
    "model_preds['HILL'] = hill_pred_cv\n",
    "\n",
    "submission_3 = submission.copy()\n",
    "submission_3['Hardness'] = model_preds.apply(np.median, axis = 1)\n",
    "submission_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "60f806f0",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "submission_3.to_csv('Median_LDA_Hill_sample_weight_sub_1.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d546f1",
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   "source": [
    "<a id=\"8\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Baseline Modeling 4.0</h1>\n",
    "\n",
    "In this section, we implement the network architecture that was presented in this [notebook](https://www.kaggle.com/code/enricomanosperti/regression-with-a-mohs-hardness-dataset) from @enricomanosperti and in this [notebook](https://www.kaggle.com/code/paddykb/pg-s3e25-evaluate-the-lgbm-prefit-technique) from @paddykb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1fb7aa97",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train.drop(columns = ['id', 'Hardness'], axis = 1)\n",
    "Y = train['Hardness']\n",
    "\n",
    "test = pd.read_csv('../input/playground-series-s3e25/test.csv')\n",
    "test = test.drop(columns = ['id'], axis = 1)\n",
    "\n",
    "skf = RepeatedKFold(n_splits = 10, n_repeats = 1, random_state = 42)\n",
    "kf = KFold(n_splits = 10, shuffle = True, random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457c6620",
   "metadata": {
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     "end_time": "2023-11-29T00:11:47.550053",
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     "start_time": "2023-11-29T00:11:47.500925",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "First, we define the loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "92694f98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:11:47.651499Z",
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   "outputs": [],
   "source": [
    "def loss_fn(y_true, y_pred):\n",
    "    \n",
    "    return tfp.stats.percentile(tf.abs(y_true - y_pred), q = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02ed19fd",
   "metadata": {
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    "tags": []
   },
   "source": [
    "Next, we define the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2491f4fd",
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    "tags": []
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   "outputs": [],
   "source": [
    "def create_model():\n",
    "\n",
    "    input_layer = tf.keras.Input(shape = (12, ))\n",
    "    x = tf.keras.layers.BatchNormalization(epsilon = 0.00001)(input_layer)\n",
    "    x = tf.keras.layers.Dense(16, activation = 'relu')(x)\n",
    "    x = tf.keras.layers.Dense(32, activation = 'relu')(x)\n",
    "    output_layer = tf.keras.layers.Dense(1)(x)    \n",
    "\n",
    "    model = tf.keras.Model(inputs = input_layer, outputs = output_layer)\n",
    "\n",
    "    model.compile(optimizer = tf.keras.optimizers.Adam(0.013, beta_1 = 0.5),\n",
    "                  loss = loss_fn)\n",
    "\n",
    "    return model\n",
    "\n",
    "callbacks_list = [\n",
    "        tf.keras.callbacks.EarlyStopping(\n",
    "            monitor = 'val_loss', patience = 30, verbose = 0, mode = 'min', restore_best_weights = True),\n",
    "        tf.keras.callbacks.ReduceLROnPlateau(\n",
    "            monitor = 'val_loss', factor = 0.8, patience = 3, min_lr = 0.00001),\n",
    "        tf.keras.callbacks.TerminateOnNaN()\n",
    "    ] \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6fc9c2c",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we run the usual cross-validation routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bf287066",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:11:48.069460Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Fold 0 ==> TF oof median absolute error score is ==> 0.4258859157562256\n",
      "--------------------------------------------------------------\n",
      "Fold 1 ==> TF oof median absolute error score is ==> 0.5161252021789551\n",
      "--------------------------------------------------------------\n",
      "Fold 2 ==> TF oof median absolute error score is ==> 0.4147052764892578\n",
      "--------------------------------------------------------------\n",
      "Fold 3 ==> TF oof median absolute error score is ==> 0.4761476516723633\n",
      "--------------------------------------------------------------\n",
      "Fold 4 ==> TF oof median absolute error score is ==> 0.5272005081176756\n",
      "--------------------------------------------------------------\n",
      "Fold 5 ==> TF oof median absolute error score is ==> 0.4814858913421629\n",
      "--------------------------------------------------------------\n",
      "Fold 6 ==> TF oof median absolute error score is ==> 0.5538642406463623\n",
      "--------------------------------------------------------------\n",
      "Fold 7 ==> TF oof median absolute error score is ==> 0.6393227577209473\n",
      "--------------------------------------------------------------\n",
      "Fold 8 ==> TF oof median absolute error score is ==> 0.45369505882263184\n",
      "--------------------------------------------------------------\n",
      "Fold 9 ==> TF oof median absolute error score is ==> 0.5294964790344237\n",
      "The TF oof median absolute error is 0.5017928981781006\n"
     ]
    }
   ],
   "source": [
    "tf_scores, tf_preds = list(), list()\n",
    "\n",
    "for i, (train_idx, test_idx) in enumerate(skf.split(X, Y)):\n",
    "\n",
    "    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
    "    Y_train, Y_test = Y.iloc[train_idx], Y.iloc[test_idx]\n",
    "\n",
    "    print('--------------------------------------------------------------')\n",
    "    \n",
    "    x_train = X_train.copy()\n",
    "    x_test = X_test.copy()\n",
    "    test_cv = test.copy()\n",
    "    \n",
    "    ##########\n",
    "    ## LGBM ##\n",
    "    ##########\n",
    "    \n",
    "    LGBM_md = LGBMRegressor().fit(X_train, Y_train)\n",
    "\n",
    "    x_train.loc[:, 'LGBM_1'] = LGBM_md.predict(X_train)\n",
    "    x_test.loc[:, 'LGBM_1'] = LGBM_md.predict(X_test)\n",
    "    test_cv.loc[:, 'LGBM_1'] = LGBM_md.predict(test)\n",
    "    \n",
    "    ########\n",
    "    ## NN ##\n",
    "    ########\n",
    "    \n",
    "    nn_md = create_model()\n",
    "    nn_md.fit(x_train, Y_train,\n",
    "              epochs = 100, \n",
    "              verbose = 0, \n",
    "              callbacks = callbacks_list)\n",
    "    \n",
    "    tf_md_pred = nn_md.predict(x_test, verbose = 0)\n",
    "    tf_md_pred_test = nn_md.predict(test_cv, verbose = 0)\n",
    "    tf_preds.append(tf_md_pred_test)\n",
    "    \n",
    "    tf_score = median_absolute_error(Y_test, tf_md_pred)\n",
    "    \n",
    "    tf_scores.append(tf_score)\n",
    "    print('Fold', i, '==> TF oof median absolute error score is ==>', tf_score)\n",
    "\n",
    "print(f\"The TF oof median absolute error is {np.mean(tf_scores)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "093a0d77",
   "metadata": {
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     "start_time": "2023-11-29T00:25:52.714427",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "Next, we aggregate the predictions using the median."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ce9f8332",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
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       "      <th>id</th>\n",
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       "      id  Hardness\n",
       "0  10407  2.393281\n",
       "1  10408  2.390134\n",
       "2  10409  5.787288\n",
       "3  10410  2.518053\n",
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   "source": [
    "tf_oof_preds = pd.DataFrame(np.concatenate(tf_preds, axis = 1))\n",
    "\n",
    "submission_4 = submission.copy()\n",
    "submission_4['Hardness'] = tf_oof_preds.apply(np.median, axis = 1)\n",
    "submission_4.head()"
   ]
  },
  {
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   "execution_count": 48,
   "id": "56772e69",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission_4.to_csv('TF_sub.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d138fd",
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   "source": [
    "<a id=\"9\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Ensemble Modeling</h1>\n",
    "\n",
    "In this section, we ensemble the predictions from the previous four baseline modelings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c276aee6",
   "metadata": {
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   "outputs": [
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      "text/plain": [
       "      id     pred1     pred2     pred3     pred4\n",
       "0  10407  2.592411  2.386626  2.402132  2.393281\n",
       "1  10408  2.544795  2.520987  2.479047  2.390134\n",
       "2  10409  6.016720  5.790057  5.818545  5.787288\n",
       "3  10410  4.313842  4.440443  4.756925  2.518053\n",
       "4  10411  5.378780  4.925160  4.954281  5.787288"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_1.columns = ['id', 'pred1']\n",
    "submission_2.columns = ['id', 'pred2']\n",
    "submission_3.columns = ['id', 'pred3']\n",
    "submission_4.columns = ['id', 'pred4']\n",
    "\n",
    "dat = pd.merge(submission_1, submission_2, on = 'id')\n",
    "dat = pd.merge(dat, submission_3, on = 'id')\n",
    "dat = pd.merge(dat, submission_4, on = 'id')\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "71337c2d",
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   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Hardness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10407</td>\n",
       "      <td>2.397707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10408</td>\n",
       "      <td>2.500017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10409</td>\n",
       "      <td>5.804301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10410</td>\n",
       "      <td>4.377142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10411</td>\n",
       "      <td>5.166531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  Hardness\n",
       "0  10407  2.397707\n",
       "1  10408  2.500017\n",
       "2  10409  5.804301\n",
       "3  10410  4.377142\n",
       "4  10411  5.166531"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['Hardness'] = dat.drop(columns = 'id', axis = 1).apply(np.median, axis = 1)\n",
    "dat = dat.drop(columns = ['pred1', 'pred2', 'pred3', 'pred4'], axis = 1)\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0e06f94d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:25:54.134377Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dat.to_csv('Ensemble_sub.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16f7ba50",
   "metadata": {
    "papermill": {
     "duration": 0.051403,
     "end_time": "2023-11-29T00:25:54.263741",
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   "source": [
    "<a id=\"10\"></a>\n",
    "# <h1 style=\"background-color:lightgray;font-family:newtimeroman;font-size:350%;text-align:center;border-radius: 15px 50px;\">Baseline Modeling 5.0</h1>\n",
    "\n",
    "As pointed out by @adaubas in this [post](https://www.kaggle.com/competitions/playground-series-s3e25/discussion/455945), this competition can be treated as a classification task. Also, @siukeitin pointed out in this [post](https://www.kaggle.com/competitions/playground-series-s3e25/discussion/457631#2540597) the 9 classes to be considered to get a 0.25 LB score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "acbadf2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:25:54.368554Z",
     "iopub.status.busy": "2023-11-29T00:25:54.368128Z",
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     "shell.execute_reply": "2023-11-29T00:25:54.438215Z"
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     "end_time": "2023-11-29T00:25:54.442184",
     "exception": false,
     "start_time": "2023-11-29T00:25:54.314739",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('../input/playground-series-s3e25/train.csv')\n",
    "train = train.drop(columns = ['id'], axis = 1)\n",
    "\n",
    "test = pd.read_csv('../input/playground-series-s3e25/test.csv')\n",
    "test = test.drop(columns = ['id'], axis = 1)\n",
    "\n",
    "submission = pd.read_csv('../input/playground-series-s3e25/sample_submission.csv')\n",
    "\n",
    "X = train.drop(columns = ['Hardness'], axis = 1)\n",
    "Y = train['Hardness']\n",
    "\n",
    "skf = RepeatedStratifiedKFold(n_splits = 10, n_repeats = 1, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "907aef60",
   "metadata": {
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     "duration": 0.051636,
     "end_time": "2023-11-29T00:25:54.545229",
     "exception": false,
     "start_time": "2023-11-29T00:25:54.493593",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we create the `labels` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dadbe9d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:25:54.653734Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "unique_targets = [1.25, 2.25, 3.05, 4.05, 4.85, 5.75, 6.55, 7.75, 9.25]\n",
    "Y_trans = pd.Series([min(unique_targets, key = lambda x: abs(x - pred)) for pred in Y])\n",
    "\n",
    "le = LabelEncoder().fit(Y_trans)\n",
    "Y_label = le.transform(Y_trans)\n",
    "Y = pd.Series(Y_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f74270",
   "metadata": {
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     "duration": 0.050387,
     "end_time": "2023-11-29T00:25:54.789900",
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     "start_time": "2023-11-29T00:25:54.739513",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we run the cv-routine as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "62a2f8b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:25:54.893554Z",
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    },
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     "duration": 17.814454,
     "end_time": "2023-11-29T00:26:12.655528",
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     "start_time": "2023-11-29T00:25:54.841074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "Fold 0 ==> RF accuracy score is ==> 0.5331412103746398\n",
      "--------------------------------------------------------------\n",
      "Fold 1 ==> RF accuracy score is ==> 0.5331412103746398\n",
      "--------------------------------------------------------------\n",
      "Fold 2 ==> RF accuracy score is ==> 0.5456292026897214\n",
      "--------------------------------------------------------------\n",
      "Fold 3 ==> RF accuracy score is ==> 0.5379442843419788\n",
      "--------------------------------------------------------------\n",
      "Fold 4 ==> RF accuracy score is ==> 0.5206532180595581\n",
      "--------------------------------------------------------------\n",
      "Fold 5 ==> RF accuracy score is ==> 0.5389048991354467\n",
      "--------------------------------------------------------------\n",
      "Fold 6 ==> RF accuracy score is ==> 0.5177713736791547\n",
      "--------------------------------------------------------------\n",
      "Fold 7 ==> RF accuracy score is ==> 0.5365384615384615\n",
      "--------------------------------------------------------------\n",
      "Fold 8 ==> RF accuracy score is ==> 0.5221153846153846\n",
      "--------------------------------------------------------------\n",
      "Fold 9 ==> RF accuracy score is ==> 0.5317307692307692\n",
      "RF oof accuracy is 0.5317570014039754\n"
     ]
    }
   ],
   "source": [
    "RF_preds, RF_scores = list(), list()\n",
    "\n",
    "for i, (train_idx, test_idx) in enumerate(skf.split(X, Y)):\n",
    "\n",
    "    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
    "    Y_train, Y_test = Y.iloc[train_idx], Y.iloc[test_idx]\n",
    "\n",
    "    print('--------------------------------------------------------------')\n",
    "\n",
    "    RF = RandomForestClassifier(n_estimators = 100, \n",
    "                                min_samples_split = 3,\n",
    "                                min_samples_leaf = 5, \n",
    "                                max_depth = 10, \n",
    "                                random_state = 1).fit(X_train, Y_train)\n",
    "    RF_pred = RF.predict(X_test)\n",
    "    RF_score = accuracy_score(Y_test, RF_pred)\n",
    "    RF_scores.append(RF_score)\n",
    "    RF_preds.append(RF.predict(test))\n",
    "    \n",
    "    print('Fold', i, '==> RF accuracy score is ==>', RF_score)\n",
    "\n",
    "\n",
    "print(f\"RF oof accuracy is {np.mean(RF_scores)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "31af0f43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-29T00:26:12.762171Z",
     "iopub.status.busy": "2023-11-29T00:26:12.761799Z",
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     "shell.execute_reply": "2023-11-29T00:26:13.448020Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Hardness</th>\n",
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       "  <tbody>\n",
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      "text/plain": [
       "      id  Hardness\n",
       "0  10407      2.25\n",
       "1  10408      2.25\n",
       "2  10409      5.75\n",
       "3  10410      5.75\n",
       "4  10411      5.75"
      ]
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     "execution_count": 55,
     "metadata": {},
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    }
   ],
   "source": [
    "results = mode(pd.DataFrame(RF_preds), axis = 0, keepdims = True)[0]\n",
    "submission['Hardness'] = le.inverse_transform(results[0])\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9359bcdd",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv('RF_classifier_sub.csv', index = False)"
   ]
  }
 ],
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